L12.0pt

# Security Approaches for Data Provenance in the Internet of Things: A
Systematic Literature Review

Omair Faraj<EMAIL_ADDRESS>Internet Interdisciplinary Institute (IN3),
Universitat Oberta de Catalunya (UOC), CYBERCAT-Center for Cybersecurity
Research of CataloniaBarcelonaSpain08018 SAMOVAR, Télécom SudParis, Institut
Polytechnique de ParisFrance ,  David Megías<EMAIL_ADDRESS>Internet
Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC),
CYBERCAT-Center for Cybersecurity Research of CataloniaBarcelonaSpain08018 and
Joaquin Garcia-Alfaro<EMAIL_ADDRESS>SAMOVAR,
Télécom SudParis, Institut Polytechnique de Paris19 place Marguerite
PereyPalaiseauFrance91120

###### Abstract.

The Internet of Things (IoT) relies on resource-constrained devices deployed
in unprotected environments. Different types of risks may be faced during data
transmission in single-hop and multi-hop scenarios. Addressing these
vulnerabilities is crucial. A systematic literature review of data provenance
in IoT is presented, exploring existing techniques, practical implementations,
security requirements, and performance metrics. Respective contributions and
shortcomings are compared. A taxonomy related to the development of data
provenance in IoT is proposed. Open issues are identified, and future research
directions are presented, providing useful insights for the evolution of data
provenance research in the context of the IoT.

Internet of Things, Data Provenance, Intrusion Detection, Data Integrity,
Cyber Security, Trustworthiness.

††ccs: General and reference Surveys and overviews††ccs: Security and privacy
Trustworthiness††ccs: Security and privacy IoT security††ccs: Security and
privacy Secure provenance

## Acknowledgments

The authors acknowledge the funding obtained from the PID2021-125962OB-C31
“SECURING” project funded by the Ministerio de Ciencia e Innovación, la
Agencia Estatal de Investigación and the European Regional Development Fund
(ERDF), as well as the ARTEMISA International Chair of Cybersecurity and the
DANGER Strategic Project of Cybersecurity, both funded by the Spanish National
Institute of Cybersecurity through the European Union – NextGenerationEU and
the Recovery, Transformation and Resilience Plan. The authors also acknowledge
the support from IMT’s CyberCNI chair (Cybersecurity for Critical Networked
Infrastructures, cf. https://cybercni.fr/).

##  1\. Introduction

Figure 1. Workflow of this paper.

An intelligent system known as the Internet of Things (IoT) comprises a
network infrastructure of physical objects that are interconnected, enabling
the exchange and collection of data from diverse sources and destinations. The
objects are designed to transfer data captured from the environment over an
insecure internet connection, where it is processed, managed, and analyzed
through various technologies (Ammar et al., 2018; Botta et al., 2016). IoT
increases the level of network vulnerabilities as it becomes more accessible
to attackers. Despite this, the intelligent system is widely applied in
different fields such as industrial control, healthcare systems, home
appliances, car automation, and environmental monitoring (Lazarescu, 2017;
Manikandan and Subha, 2018). These systems rely on various sensors for data
acquisition and transmission, which are resource constrained due to deployment
in an unprotected physical environment. IoT networks operate via single-hop
and multi-hop structures, with data transmission vulnerable to tampering
(Gubbi et al., 2013). Furthermore, the data generated from a huge number of
sensor node sources is subjected to in network processing by intermediate
nodes during transmission to a base station, and this processed data is used
in the decision making processes, making it critical to evaluate data
trustworthiness, detect attacks during data generation and processing, and
identify nodes and actions responsible for those attacks (Sultana et al.,
2015). IoT systems lack the capacity to perform identity management, ensure
data trustworthiness, detect abnormal behavior, and control unauthorized data
access, becoming vulnerable to cyber attacks (Hu et al., 2020). Data
provenance solutions have been proposed aiming to contribute to overcome the
aforementioned security issues in IoT systems. Initially, data provenance was
used for heterogeneous database systems (Wang and Madnick, 1990). It was
originally introduced to document the origin, history, derivation, creation
and usage of some entity, which is also known as lineage (Wang et al., 2016b).
It explains how data evolves from process to another. Many researchers used
provenance in the field of information technology, which is defined as the
process of recording the history of origin, evolution, process activities and
manipulation of data over time (Buneman et al., 2001a; Simmhan et al., 2005;
Zafar et al., 2017). Researchers introduce data provenance as a solution to
provide a number of required features to ensure trustworthiness, integrity,
data quality, confidentiality, availability and other security requirements.
In this regard, data provenance is presented in many domains such as file
systems, databases, cloud computing, IoT, distributed networks and many more
domains. This research focuses on data provenance within the IoT and Wireless
Sensor Networks (WSN) domains.

1.1 Motivation — In IoT networks, data provenance provide the capability to
ensure data trustworthiness by summarizing the history of ownership and
actions performed on collected data from the source node to the final
destination. Provenance must be recorded for each collected data packet from
source nodes and track the forwarding nodes engaged in the process of data
transmission from data origin to destination, but many challenges arise to
deploy such a solution. A major challenge is the rapid increase of provenance
during the transmission phase in IoT networks. Furthermore, there are
limitations in data storage capabilities, bandwidth and energy constraints of
nodes. Hence, it is critical to address security requirements in such
challenging insecure environment in order to securely and efficiently capture
provenance information for each packet. Although data provenance has been
studied extensively for database management systems it is still not widely
addressed in IoT networks. Also, the literature still lacks a systematic
literature review which introduces the existing proposed data provenance
approaches in IoT networks and provide a detailed analysis in terms of
security requirements, provenance encoding and storing techniques, encountered
attacks and performance metrics for the evaluation of proposed methods. We
organize this review using the taxonomy shown in Figure 1. Our work aims to
provide researchers with the most practical implementations over the past
decade to point out open issues and research challenges with in-depth insights
for future directions in the field of data provenance in IoT based on a
Systematic Literature Review (SLR).

1.2 Major Contributions — The main contributions of our survey are summarized
below:

  * •

We present detailed overview of data provenance and its related concepts,
offering essential background knowledge on the security aspects. We also point
out potential attacks to data provenance models in IoT. We describe the
different types of provenance, storage mechanisms and security requirements.

  * •

We propose an in-depth review of existing techniques for data provenance in
IoT networks with their advantages, limitations and a set of review attributes
through a systematic literature review process.

  * •

We provide a detailed overview of the encountered attacks and performance
metrics used to evaluate security requirements and system efficiency. We
propose a taxonomy including different attributes related to the development
of data provenance in IoT.

  * •

We list open issues and research challenges on the evolution of data
provenance research in IoT.

1.3 Paper Organization — Section 2 presents the review methodology used to
conduct our survey. Section 3 provides an overview on IoT network and data
provenance. Section 4 presents existing surveys on data provenance in IoT.
Section 5 introduces existing data provenance approaches in IoT networks, with
a comparative analysis. Section 6 provides a discussion. Section 7 presents
open issues and research challenges. Section 8 concludes the paper.

##  2\. Review methodology

A Systematic Literature Review (SLR) is a thorough and well-organized method
used to identify, assess, and bring together existing research studies on a
specific topic. It follows a precise and transparent process to ensure that
all relevant studies are included. By following a systematic and well-defined
procedure, this type of study offers many advantages. Firstly, systematic
mappings enable a comprehensive overview of the current status of research in
a particular area in the state of the art. Secondly, it facilitates the
identification of gaps in the literature and provide evidence to guide further
research, thereby preventing redundant efforts. Systematic mappings allow for
a high-level analysis of all available studies within a specific domain,
offering insights into broad research questions concerning the current state
of the literature (Petersen et al., 2008; Kitchenham et al., 2011). A SLR is
conducted based on three main phases. The planning phase defines research
questions, and provides the search strategy and the criteria to select the
relevant state of the art studies. The execution phase identifies and selects
the required studies according to the previous phase. The reporting phase
analyzes selected studies and points out open issues and future directions.
Our review is based on a SLR methodology proposed by Kitchenham et al. (2009).
Figure 2 shows the different phases followed to conduct our review.

2.1 Research questions — We use the SLR process shown in Figure 2 to provide a
comprehensive and exhaustive synthesis of the used methodology. The primary
purpose of our review is to investigate the integration of data provenance
into IoT networks. To plan the review, we formulated six research questions as
described in Table 1.

Table 1. Research questions for the SLR.

# |  Question |  Rationale |  Where to be addressed?
---|---|---|---
RQ1 |  How data provenance is linked to IoT networks? |  The answer will help understand the main purpose behind using data provenance in IoT networks |  Section 3
RQ2 |  What are the provenance storage techniques, attacks and security requirements when integrating provenance with IoT? |  The answer will help researchers know the different provenance storing techniques, security attacks against IoT system and needed security requirements when implementing a provenance solution |  Section 3
RQ3 |  How can data provenance security approaches for IoT networks be categorized? |  The answer will help understanding the classification of proposed methods for the data provenance |  Section 5
RQ4 |  What are the existing practical implementations of data provenance in IoT? |  The answer will provide researchers with a comprehensive understanding of the shifts in the literature over the past decade. |  Section 5
RQ5 |  What are the advantages and limitations of the proposed techniques for data provenance in IoT in the studied literature? |  The answer will help specify the contributions and shortcomings of the studied state of the art |  Section 5 and 6
RQ6 |  What are the main research gaps and challenges in the domain of data provenance in IoT networks? |  The answer will highlight the open issues, and help researchers to identify research challenges and possible future directions. |  Section 7

Table 2. Electronic databases used in the search strategy. Database |  URL
---|---
IEEEXplore |  http://ieeexplore.ieee.org
Science Direct |  http://www.sciencedirect.com
Scopus |  http://www.scopus.com
Web of Science |  http://www.webofknowledge.com
ACM Digital Library |  https://dl.acm.org/
Springer Link |  https://link.springer.com/

2.2 Search strategy — To address the research questions, a search process was
conducted across six electronic publication databases. The selected databases
are shown in Table 2. These databases are widely recognized and widely used in
the fields of computer science and engineering, offering comprehensive
coverage of the relevant literature. The automated search process aimed to
retrieve relevant studies from these databases (Chen et al., 2010; Malheiros
et al., 2007). The set of keywords used to find these studies include the
following: _Internet of Things,_ _Wireless Sensor Network,_ _Data Provenance,_
and _Secure Provenance_. The search strategy in the mentioned electronic
databases was performed using the following query (and considering synonyms
and alternative terms111The primary keywords were linked together using the
logical operator “AND” to establish the search parameters. Similarly, the
variations and synonyms were connected using the logical operator “OR” to
broaden the scope of the search. ):

("Data Provenance" OR "Secure Provenance" OR "Provenance")
---
AND
("Internet of Things" OR "IoT" OR "Wireless Sensor Network" OR "WSN")

2.3 Selection criteria — To assess the relevance of each primary study in
relation to the defined research questions, different selection criteria were
applied. The objective was to include studies that have the potential to
address the research questions and exclude those that do not contribute to
answering them. The criteria is used as a means of determining the suitability
of each study for inclusion in the analysis. We have considered several
inclusion and exclusion criteria as follows:

Inclusion Criteria |  |  Exclusion Criteria
---|---|---
IC1: Articles that explores and addresses scenarios, research challenges, and potential opportunities concerning the integration of data provenance and IoT. |  |  EC1: The article is not related to data provenance.
EC2: The article is not focused on data provenance in the domain of IoT or
WSN.
IC2: All articles that have original work and used any security data provenance approach and belong to the IoT or WSN domain. |  |  EC3: The article serves as an earlier version of a more recent investigation conducted on the same research topic.
IC3: Publication years in the range 2013–2023.
IC4: Articles that are out of the specified time range but have high citations
(above 30 citations).
IC5: Articles written in English.
IC6: Articles published in journals, conferences and workshops. |  |  EC4: The article is not written in English.
EC5: The full text of the article is unavailable.
EC6: Articles that are repeating in different resources, then, duplicate was
excluded.

Figure 2. Review methodology phases of our Systematic Literature Review (SLR).

2.4 Article selection and data extraction — Initially, a search was conducted
in the chosen electronic databases based on the search strategy, leading to a
total of 2706 papers. The search and selection processes involved considering
primary studies published between 2013 and 2023. The reasons for choosing this
time range are to make the reference list easier to handle and to avoid older
protocols, which usually perform lower than newer approaches, and most
approaches are an enhancement of them. Two papers were selected from 2010 (Lim
et al., 2010) and 2011 (Sultana et al., 2011) for being highly cited papers in
this field and their direct link to the scope of our research.

To ensure compatibility with different database engines, slight modifications
were made to the search string. The automated search was then conducted on
each electronic database, focusing on the title, abstract, and keyword fields.
Subsequently, a screening process was carried out on the titles and abstracts
to identify and exclude duplicate and irrelevant papers. This screening
process led to a reduction in the number of relevant papers to 107. The
criteria used for exclusion were effective in removing irrelevant studies from
this review. Further analysis is conducted on the full texts of these papers,
resulting in the exclusion of 67 papers. The remaining 40 papers were included
in this review. Figure 2 shows the complete identification and selection
process of our SLR.

To extract data from the chosen primary studies, a data extraction procedure
was conducted. This procedure points out items specifically related to the
research questions as well as other relevant information. Data extraction was
performed on each paper to ascertain the following information: (1) the
security technique used to secure provenance, (2) the advantages and
limitations of this solution, (3) the approaches and technologies applied to
implement provenance encoding, decoding and storing, (4) the metrics used to
evaluate the proposed method, and (5) the attacks studied by researchers in
the security analysis process of their proposed solution.

##  3\. Data provenance in IoT networks

This section provides an overview of IoT networks, discussing the attacks on
those systems and the necessary security requirements for their design and
implementation. Additionally, we introduce the concept of data provenance as a
general term and explore its main applications and implementations in IoT
systems.

3.1 Overview of IoT networks — The realization of the IoT concept in practical
terms can be achieved by combining various enabling technologies. In this
survey, we direct our attention towards sensor networks, which stand out as a
highly used technology across a wide range of applications. Sensor networks
have been proposed for numerous scenarios, including environmental monitoring,
e-health, intelligent transportation systems, military applications, and
industrial monitoring , among others. These networks consist of a number of
sensing nodes that communicate using a wireless multi-hop model. Typically,
nodes transmit captured data to a limited number of intermediate forwarding
nodes (Atzori et al., 2010). In recent years, scientists have conducted
extensive research on sensor networks, examining various challenges across
different layers of the network protocol stack (Akyildiz et al., 2002). It
faces significant vulnerability to attacks due to several factors. Firstly,
many of its components are often in stand-by mode for extended periods, making
them easy targets for physical attacks. Secondly, the widespread use of
wireless communication in the IoT makes eavesdropping a simple task. Lastly,
most IoT components possess limited capabilities in terms of energy and
computing resources, which prevents the implementation of complex security
schemes. Specifically, trustworthiness and data integrity present major
security concerns (Atzori et al., 2010). The presence of diverse data sources
requires the establishment of trustworthiness for the data, ensuring that only
reliable information is taken into account during the decision-making process.
In addition, data integrity solutions play an important role in ensuring that
any unauthorized modifications to data being transmitted by source nodes or
intermediate nodes are detected by the system, preventing adversaries from
tampering with the information. For these concerns, new solutions were
proposed to ensure the trustworthiness of data, such that any decision process
is based on trustworthy data. Data provenance is introduced as one of these
effective solution that can be used in constrained networks to ensure
trustworthiness of sensed data. Data provenance techniques must consider
additional methods to tackle the problem of provenance integrity and data
packet integrity.

Figure 3. Taxonomy of Data Provenance in IoT networks.

3.2 What is data provenance? — The definition of provenance has undergone
changes over time, adapting to different application contexts. In database
systems, provenance was traditionally referred to as lineage and focused on
identifying the source of data resulting from query processing, commonly
referred to as data provenance (Pan et al., 2023; Moreau et al., 2018). Data
lineage was initially formalized by Cui et al. (2000), specifically in the
field of relational databases. It aimed to trace each tuple $"t"$ in the input
tables that played a role in generating the output of a query from the
database (Cheney et al., 2009). Provenance, also referred to as pedigree, or
genealogy, is a form of metadata that documents the origin and utilization of
a given entity (Simmhan et al., 2005; Glavic and Dittrich, 2007; Buneman et
al., 2001b). In the field of information technology, provenance considers data
as the counterpart or reflection of an art object. In this field, researchers
also define provenance as the complete information about the entire process of
data generation and evolution over time. This includes capturing the static
origins of data as well as tracking their dynamic changes throughout their
transmission process (Gao et al., 2010). As a summary, provenance holds the
capability to provide insights into the what, where, when, how, and why
aspects of a given data object.

3.3 Data provenance integration with IoT — The interconnectivity of the IoT
has brought significant improvements to a wide range of different application
scenarios. It has also introduced security and privacy concerns due to data
transmission over open networks. Attackers can easily inject malicious data
into the transmission path and maliciously manipulate data without any notice
from the entities of the networks, thereby compromising the integrity and
trustworthiness of the data. Additionally, the rapid spread of malicious data
can cause catastrophic failures. Moreover, the sharing, transmission, and
processing of data leads to the risk of user privacy breaches. Data
provenance, which records the origin and processing history of data, presents
a potential solution to address these aforementioned issues. Provenance
information can record the original source node which produced or forwarded
data packet and the operations which the data went through. In many cases,
provenance can provide the time and location of the entities that engaged in
any operational procedure on data. We provide researchers with a proposed
taxonomy for the different aspects of data provenance in IoT as shown in
Figure 3.

The provenance of a data item $d$, denoted as $P_{d}$, is outlined in
Definition 1. $P_{d}$ records information about the origin and the series of
data actions a data item undergoes during its transmission from source to its
final destination. There are different types of provenance according to the
position of nodes in the network such as simple provenance, as shown in Figure
4; aggregate provenance, as shown in Figure 4; and different data path from
the same source, as shown in Figure 4.

######  Definition 0.

Given a data packet $d$, the provenance $P_{d}$ is a graph $G(V,E)$ satisfying
the following properties: 1) $P_{d}$ is a subgraph of the sensor network
$G(V,L)$; 2) for $v_{i},v_{j}\in V$, $v_{i}$ is a child of $v_{j}$ if and only
if $\mathrm{HOST}(v_{i})=n_{i}$ participated in the distributed calculation of
$d$ and/or forwarded the data to $\mathrm{HOST}(v_{j})=n_{j}$, whereby:

  * •

$N$ = {$n_{i}/n_{j}\,|\,n_{i}/n_{j}$ is a network node of identifier is
$i,j$}: a set of network nodes.

  * •

$E$ = {$e_{i,j}\,|\,e_{i,j}$ is an edge connecting nodes $n_{i}$ and $n_{j}$}:
a set of edges connecting nodes.

  * •

$\mathrm{HOST}(\cdot)$: The function that assigns a host node to each network
node in $V$.$\mathrm{HOST}(v_{i})$ denotes the host node of the network node
$v_{i}$.

[] [] []

Figure 4. Data provenance. (a) Simple provenance. (b) Aggregate provenance.
(c) Provenance with different data path from one source.

[] [] [] []

Figure 5. Provenance storage mechanisms. (a) Local database. (b) In-packet
storage. (c) Blockchain-based. (d) Cloud-based.

3.4 Data provenance storage — Efficiently managing the increasing granularity
of captured information in provenance, as the number of sensor nodes in a
network grows, is a crucial demand. In IoT, the provenance expands rapidly due
to the increase participation of forwarding nodes. The elements of provenance
information vary across applications and methods, depending on the specific
requirements fulfilled by the proposed solution. Furthermore, gathering
detailed information about data generation, processing, and forwarding enables
the system to ensure diverse security requirements. Therefore, it is essential
to store this data efficiently to minimize bandwidth, storage, and energy
overhead. Provenance information can be stored using four main storage
techniques: local database, blockchain, in-packet storage, and cloud-based.

  1. (1)

Local Database: In the local database system, provenance information is stored
either in a distributed or centralized database, depending on the deployed
solution’s application. This storage system has challenges regarding storing
and querying flexibility, as well as how IoT nodes store data information at
each forwarding node. Figure 5 shows an example of the use of a local database
in an IoT network.

  2. (2)

In-packet: The second storage technique involves embedding the provenance
records within the data packet and maintaining the provenance chain throughout
its data path to the final destination as shown in Figure 5. As previously
mentioned, when data items are processed and transmitted across large-scale
systems, the size of the provenance can significantly exceed the size of the
data itself. For example, according to the findings of Jayapandian et al.
(2006), in the proposed MiMI system, the provenance associated with data of
size 270 MB amounts to approximately 6 GB in size (Jayapandian et al., 2006).
This limits the inclusion of numerous information elements in the provenance
record, and different provenance systems must selectively retrieve certain
provenance information. In many applications, it may not be feasible to ensure
security requirements by limiting the provenance information to a very small
size which makes it challenging to achieve the required security conditions.

  3. (3)

Blockchain-based: Blockchain, a peer-to-peer network’s decentralized ledger
maintained by all peers, which offers distributed data storage and has been
applied to establish data provenance by recording data processes as blockchain
transactions (Salman et al., 2019). The large number of data packets generated
from different source nodes in the IoT network complicates the use of
blockchain for storing provenance records, as each record becomes a
transaction. This complexity introduces challenges and limitations when
applying blockchain for provenance storage. An example of connecting a
blockchain to an IoT network for data provenance is shown in Figure 5.

  4. (4)

Cloud-based: In the context of IoT applications, cloud computing is often used
to store provenance records as shown in Figure 5. Cloud storage provides
several advantages for managing and storing provenance records in IoT. Cloud
storage solutions can easily scale to accommodate the massive volumes of data
generated by IoT devices. As the number of connected devices increases, cloud
platforms can dynamically allocate resources to handle the growing data
storage requirements. It is also designed to be compatible with various IoT
devices and platforms. This ensures that provenance records from diverse
sources can be efficiently stored, managed, and retrieved. Integrating cloud
storage with existing IoT architectures and applications can be complex.
Compatibility issues, API variations, and the need for uninterrupted data flow
between devices and the cloud may introduce integration challenges. Moreover,
IoT devices generate huge amount of data, and transferring this data to and
from the cloud can introduce latency issues. The efficiency of cloud storage
solutions relies on the speed and reliability of data transfer, which can be a
challenge, basically in real-time applications (Li et al., 2014; Isaac Abiodun
et al., 2022; Zhang et al., 2021; Zawoad et al., 2018). An example of
connecting a cloud database system to an IoT network for data provenance is
shown in Figure 5.

_3.4.1 Data provenance categories._ The main objective when implementing a new
data provenance method is to securely store and transmit provenance
information. To do so, many challenges in term of security and privacy need to
be addressed, which is related to provenance manipulation in collection,
storing, and transmission. It is also essential for the designed system to
consider the security requirements to overcome such challenging issues (Liu
and Wu, 2020; Pan et al., 2023). In this context, secure provenance solutions
can be divided into the following categories: Cryptography-based, Digital
Watermarking, Bloom filters, Physical Unclonable Functions, Fingerprints,
Blockchain-based, Frameworks with storing methods, Data Santization, Lexical
chaining, Graph-based, Path difference and Logging-based. These categories
includes different security techniques that may be combined in some
applications to assure data provenance in different IoT scenarios. These
methods rely on many factors to be developed such as network resources, IoT
application, needed security requirements, provenance storage approach, energy
utilization, storage overhead, attack types, and network architecture. The
categorization of security techniques for data provenance is shown in the
proposed taxonomy in Figure 3. Moreover, a detailed definition and overview on
the different categories with their respective techniques is provided in
Section 5.

3.5 Security Requirements — Introducing the challenges of ensuring data
trustworthiness in such limited and constrained networks have set a number of
security requirements that should be satisfied to have a robust system against
different types of attacks. These requirements differ from one scenario to
another and are based on the application of the IoT system. The requirements
are described as follows:

  * •

Data Integrity: If the provenance information is altered, it becomes
impossible to perform accurate identity management, leading to delayed
detection of faulty data propagation. To prevent attackers from manipulating
provenance information by selectively adding or removing information, it is
important to maintain the integrity of the provenance information (Hu et al.,
2020).

  * •

Confidentiality: It means that any potential adversary is unable to extract
any details about the origin or content of a data packet just by examining the
packet’s data information and metadata (Hu et al., 2020).

  * •

Availability: In general, availability refers to the ability to access data
and is often ensured through fault-tolerance mechanisms like data replication
across multiple locations. In the context of provenance, availability has been
considered a part of integrity. This means that integrity verification is
required to confirm, for instance, that provenance data has not been altered
(Hasan et al., 2009a; Rangwala et al., 2016) or intentionally deleted in a
selective manner (Jamil et al., 2018; Wang et al., 2012).

  * •

Privacy: Provide extra guarantees that the sources of this provenance data are
not tracked by unauthorized parties. In many cases, the provenance information
is more important than the data itself. It requires protecting privacy of such
data.

  * •

Freshness: Data freshness refers to the condition where the data is in its
most recent and up to date form. This guarantees that no adversary can replay
previous information to deceive a gateway or base station. Sensor data
measurements are consistently updated, enabling the gateway to rely on the
current state of the environmental data, thus preventing reliance on replayed
data packets.

  * •

Non-repudiation: Non-repudiation ensures that a user cannot deny their
participation in any activity once the data provenance is recorded. In some
cases, both parties need to follow a provenance commitment protocol to ensure
mutual non-repudiation. This protocol stops either party from denying their
actions or participation in the recorded activities (Asghar et al., 2012;
Backes et al., 2016).

  * •

Unforgeability: The most critical aspect of a secure provenance system is
unforgeability. Unforgeability means that any adversary attempting to alter an
existing provenance record or introduce a new forged record will be unable to
do so undetected. In other words, unforgeability provides tamper evidence,
ensuring the integrity and authenticity of the provenance records (Asghar et
al., 2012; Bany Taha et al., 2015; Akram and Ko, 2014).

3.6 Security Attacks — IoT networks are vulnerable to active and passive
attacks. The proposed security techniques takes into account these attacks and
try to prove their robustness against it through many proposed solutions using
different technologies. Provenance information may be more sensitive than the
data itself, which makes it a priority for attackers. Attacks on such systems
are of two types: attacks on data and attacks on provenance. Next, we outline
some representative attacks that may be perpetrated in an IoT environment
against data and provenance.

_3.6.1 Data Attacks_

  * •

Packet drop attack: Is a security threat where an adversary intentionally
discards or drops specific data packets within the network. This malicious act
disrupts communication between IoT devices and can lead to various issues,
such as data loss, delayed information delivery, or service unavailability. As
IoT devices often rely on wireless communication and may operate in resource-
constrained environments, packet drop attacks can significantly impact the
overall system’s functionality and reliability.

  * •

Packet replay attack: Is a type of security attack where an adversary
intercepts and records data packets as they travel through the network. The
attacker then replays or re-sends these recorded packets at a later time,
attempting to deceive the system into thinking the information is fresh and
legitimate. This malicious action can lead to various security issues, such as
unauthorized access, data manipulation, or false triggering of actions based
on the replayed data.

  * •

Data forgery: Is a form of security attack where an adversary manipulates or
alters the data transmitted between IoT devices. The attacker may forge false
sensor readings, control commands, or other critical information to deceive
the system or cause malicious actions. This type of attack can lead to
incorrect decisions, unauthorized access, or compromised system integrity.

  * •

Data modification attack: Is a type of security attack where an unauthorized
entity alters or modifies the data being transmitted between IoT devices. The
attacker may tamper with sensor readings, control commands, or other critical
information, leading to incorrect decisions or actions within the system. This
can result in operational disruptions, potential risks, or unauthorized access
to sensitive data.

  * •

Eavesdrop: Refers to the unauthorized interception and monitoring of
communication between IoT devices. A malicious attacker, also known as an
eavesdropper, captures and listens to data packets as they traverse the
network. By doing so, the eavesdropper can access sensitive information, such
as sensor readings, control commands, or personal data, which can lead to
privacy breaches, security vulnerabilities, and potential misuse of the
intercepted data.

_3.6.2 Provenance Attacks_

  * •

Provenance record drop attack: An individual with malicious intent or a group
of colluding users can intentionally drop specific provenance records or even
the entire set of captured provenance information from a system.

  * •

Provenance replay attack: An attacker could attempt to deceive the system by
replaying a previously recorded provenance record at a later point, thereby
manipulating the integrity of the provenance chain.

  * •

Forging provenance attack: In the context of provenance records, a malicious
user or a group of colluding users can engage in forgery by creating false
data entries. These forged records can be inserted between legitimate
provenance records or added at the end of the existing chain of provenance.
The attacks that involve adding false records at the end are commonly known as
append attacks. When multiple consecutive adversaries are involved, the
process of forging and adding records can be further simplified, as they can
only insert forged provenance between themselves.

  * •

Provenance modification attack: The attacker’s objective is to manipulate the
provenance record by introducing false data, removing specific information, or
modifying existing information to deceive the system.

  * •

Provenance chain tampering: Provenance records are linked together in a chain
to create a complete provenance information of a specific data packet. An
attacker’s goal is to alter the order of this chain and modify its contents,
thereby attempting to manipulate the integrity and reliability of the
provenance information.

  * •

Inference attack: An inference attack compromises the privacy of provenance
data, enabling an adversary to deduce sensitive information about the sources
and methods used to collect the provenance information.

##  4\. Existing surveys

Several review articles address the issue of data provenance in IoT networks.
Most of these articles focus on the security requirements for data provenance
in IoT. In this section, we compare and highlight the importance and need to
conduct this review paper. Table 3 provides a comparison between the different
review articles based on a number of aspects, including time range (represent
the starting and ending years of the conduct review), review methodology (Does
the article follows a specified review methodology to conduct the review?),
taxonomy (Does the authors provide researchers with a taxonomy that summaries
the different aspects used in the review?), security requirements (Does the
review use and elaborate the needed security requirements in data provenance
approaches?), attacks (Does the review analyze the different approaches based
on the security attacks?), performance metrics (Does the review analyze the
selected articles based on performance metrics used for the evaluation of any
data provenance technique?) and the application domain that the review article
covers.

Wang et al. (2016b) provide a deep understanding of provenance schemes,
categorizing them into five main categories: distributed schemes, elementary
schemes, lossy compression schemes, lossless compression schemes, and block
schemes. The survey primarily focuses on WSN networks, examining the
techniques used and discussing their respective advantages and disadvantages.
One notable limitation of the study is the absence of considerations regarding
attacks and performance metrics in relation to the reviewed works. Another
survey that categorizes data provenance techniques is presented by Hu et al.
(2020). The categorization is based on three main technologies logging-based,
cryptography-based and blockchain-based technologies. The authors presented
the provenance techniques based on the technology used and the requirements to
achieve a secure provenance framework. Zafar et al. (2017) conducted a
comparative analysis in their work, presenting a detailed taxonomy of secure
provenance schemes. They further perform a discussion of existing schemes,
focusing on their strengths and weaknesses. The survey provides a deep
understanding of the concept of provenance and its lifecycle, while addressing
the necessary security requirements for such systems. The survey does not
mention the performance metrics used in the studied methods. Alam and Wang
(2021) propose a comprehensive survey that present and analyze different
research methods on three aspects that are provenance management, capture, and
analysis. They additionally address the problems of maintaining data
integrity, identifying attack chains in trigger-action platforms, and policy
compliance in provenance systems of IoT environments.

A comprehensive review on data provenance collection and security in
distributed environments is presented by Ametepe et al. (2021). The work
classifies provenance schemes based on provenance collection schemes, general
provenance schemes, and basic provenance schemes. The paper discusses
provenance security elements based on integrity, confidentiality and
availability, but does not discuss provenance in IoT environemnt. Gultekin and
Aktas (2022) provide an SLR-based methodology on data provenance in IoT. The
work selects 16 papers for the study. These papers are discussed based on the
main subject that the study focus on. The survey lacks comparative analysis
based on security requirements and other metrics that are needed to evaluate a
provenance system. The most recent survey on data provenance is provided by
Pan et al. (2023). The study explores the significance of data provenance in
the domains of security and privacy. Additionally, the authors outline the
foundational principles and models of data provenance, while investigating the
mechanisms proposed by previous studies to achieve security objectives.
Furthermore, they conduct a comprehensive review of existing schemes aimed at
securing the collection and manipulation of data provenance, commonly referred
to as secure provenance and the role of data provenance, specifically in terms
of threat provenance. The study covers various application domains beyond IoT,
including file systems, databases, generic systems, cloud computing, data
management, distributed networks, and media data. While the survey examines
many of the requirements for provenance systems, it does not specifically
address the performance evaluation, deployment technologies and storage
methods of provenance techniques in IoT environments. Furthermore, it lacks a
comparative analysis in this regard.

The only review paper (Gultekin and Aktas, 2022) using a Systematic Literature
Review (SLR) methodology for article selection does not address important
aspects considered in our work, such as security requirements, security
attacks, performance metrics, provenance storage methods, and taxonomy
representation. In their review, the authors selected 16 papers, categorized
into five main categories: blockchain, security, data flow, data
trustworthiness, and privacy. However, the review lacks specific details on
data provenance in IoT and does not provide discussions on open issues,
research challenges, and future directions. Furthermore, a comparative
analysis between the selected articles is not provided. All of the other
review articles do not consider all the necessary requirements and aspects
essential for analyzing security techniques proposed for data provenance in
IoT. Our survey focuses on IoT environments and provides an extensive
comparative SLR review taking into account all the missing aspects from the
discussed literature.

Table 3. Comparison with other review papers in the literature. Reference | Year | Time | Review | Taxonomy | Security | Attacks | Performance |  Application
---|---|---|---|---|---|---|---|---
| Range | Methodology | Requirements | Metrics |  Domain
Wang et al. (2016b) | 2016 | N/A | ✗ | ✗ |  1F5F8 | ✗ | ✗ |  WSN
Zafar et al. (2017) | 2017 | N/A | ✗ |  1F5F8 |  1F5F8 |  1F5F8 | ✗ |  Cloud computing, WSN, IoT, smartphones
Hu et al. (2020) | 2020 | N/A | ✗ | ✗ |  1F5F8 | ✗ | ✗ |  IoT
Alam and Wang (2021) | 2021 | N/A | ✗ | ✗ |  1F5F8 | ✗ | ✗ |  IoT
Ametepe et al. (2021) | 2021 | 2010-2018 | ✗ |  1F5F8 |  1F5F8 | ✗ | ✗ |  Distributed Environments
Gultekin and Aktas (2022) | 2022 | 2012-2022 |  1F5F8 | ✗ | ✗ | ✗ | ✗ |  IoT
Pan et al. (2023) | 2023 | 2009-2022 | ✗ | ✗ |  1F5F8 |  1F5F8 | ✗ |  General
This review | 2023 | 2013-2023 |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 |  1F5F8 |  WSN/IoT

##  5\. Security techniques for data provenance

Data provenance is a concept that is applied in many fields of study. It is
uniquely defined by each application domain (Park and Heidemann, 2008). Data
provenance in IoT networks serves to guarantee data trustworthiness by
collecting the lineage of ownership and actions executed on collected data
from the source node to its final destination. It is essential to record
provenance for every data packet generated from source nodes and trace the
involvement of forwarding nodes throughout the data transmission process, but
deploying such a solution presents numerous challenges. One significant
challenge is the rapid increase in provenance data during the transmission
phase in IoT networks. Additionally, limitations arise from the constraints
imposed by data storage capabilities, bandwidth, and energy consumption of
nodes (Lim et al., 2010). Data provenance methods establish user trust in the
received information by validating its origin, ensuring that data packets were
generated by the designated and authorized IoT node at the specified time and
location (Bertino, 2018). Provenance can be described as a chain of nodes
which traverses from source to destination as shown in Figure 7.

The idea of data provenance has been applied by many researchers for
identifying the source of data, trace ownership to ensure data authenticity,
and evaluate trustworthiness. We classify and study the provided solutions
based on the following main technologies: Cryptography-based, Bloom Filters,
Link Fingerprints, Blockchain, Watermarking, Physical Unclonable Functions,
graph-based provenance, data sanitization, lexical chaining, path difference,
logging-based and frameworks that uses different storing methods. In Appendix
A, we provide a detailed comparison of the selected papers based on a number
of protocol metrics, performance metrics, security requirements, and attacks
(cf. Tables 4, 6, 7, and 8, respectively).

5.1 Cryptography-based Techniques — Various cryptographic techniques,
including symmetric and asymmetric cryptography methods, hash functions, and
digital signatures, are applied to design and implement systems for
identifying the source of data and ensuring the integrity of both data and
provenance. These concepts are used to propose solutions for data provenance
in many applications. The literature includes the largest number of these
solutions when dealing with cryptographic approaches. The trustworthiness of
the data items in a sensor network is evaluated by Lim et al. (2010). They
compute trust scores for sensor nodes and data packets by using data
provenance. These scores provide a method of indicating the level of
trustworthiness of both nodes and data items. This method provide security and
trustworthiness for sensor networks, yet they fail to address the challenge of
retrieving data provenance against various attacks. Moreover, a dictionary-
based secure provenance approach for WSN is proposed by Wang et al. (2016a).
They embed path indexes in the provenance instead of the actual data path by
using packet path dictionaries. Therefore, compared to the current lossy
provenance systems, the compressed provenance size in the proposed lossless
approach is smaller. To achieve security requirements such as integrity,
availability and authenticity of provenance, the AM-FM sketch method and a
robust packet sequence number generating technique are used in the system.
Provenance records are encoded at nodes that engage in every stage of data
processing and transmission using the suggested dictionary-based system. A
secure message authentication code integrates the packet and its provenance
together to provide security against any unauthorized change. After verifying
the Message Authentication Code (MAC) during decoding, the BS retrieves the
packet’s provenance graph.

While the majority of previous studies have concentrated on how to protect
against data manipulation in Home Area Networks (HAN) networks, Chia et al.
(2017) introduce a security topic that has received less attention in such
networks, namely data provenance. To ensure that the stated energy usage is
actually consumed and is gathered from the specified node at the exact
location, they provide a unique technique based on threshold cryptography and
Shamir’s secret sharing (Shamir, 1979). The authors describe a unique use of
secret sharing schemes for home energy monitoring networks, in which a secret,
or a single private key, is distributed among all the members who provide data
on energy consumption. This system also addresses the issue of location
verification. To achieve this, authors incorporate a location generator as an
additional component. The location generator employs trilateration techniques
based on Received Signal Strength Indicator (RSSI) values and RSSI filtering
methods to verify the location of the device. This not only ensures that the
right power source is under observation but also detects any potential device
relocation. Also, Suhail et al. (2018) provide a solution to the challenge of
integrating IoT with a system that is aware of data provenance, enabling the
tracking of data flow across nodes and monitoring data transformations applied
by nodes in the network. They introduce a lightweight method for transmitting
provenance information for IoT sensor data. This method encodes data
provenance information using a hash chain scheme as it traverses each
participating node, with the final provenance verification taking place at the
destination node. The technique is implemented within the context of the IPv6
Routing Protocol for Low-Power and Lossy Networks (RPL) (Alexander et al.,
2012) in Contiki OS (Dunkels et al., 2004). Each generated data packet
includes three fields: packet sequence number, data payload and provenance
information (i.e. hash value of node ID). Furthermore, Xu et al. (2019)
present a provenance approach for WSN based on path index differences, where a
new packet path is encoded using the index of a highly similar backbone path
and the differences between them. Even if the topology of the WSN is unstable,
this technique may reach an increased provenance compression rate compared to
the dictionary based provenance scheme (Wang et al., 2016a). In the presented
backbone path selection approach for WSN, the gradient of the node on the data
packet path is also determined. In order to cover the commonly used packet
transmission channels, the authors also develop a comparable backbone path
elimination technique for WSN. To find the packet pathways with the highest
similarity in the dictionaries, they develop a locality-sensitive hashing
(LSH), SimHash (Buyrukbilen and Bakiras, 2014; Charikar, 2002), based
similarity comparison algorithm. The proposed approach is evaluated according
to Total Energy Consumption (TEC) and Average Provenance Size (APS).

Moreover, Suhail et al. (2020) present an approach called Provenance-enabled
Packet Path Tracing for IoT devices connected through the RPL protocol. Their
scheme involves including sequence numbers into the routing entries of the
forwarding nodes’ routing table, establishing a node-level provenance.
Additionally, they introduced a system-level provenance that encompassed
destination and source node IDs, enabling complete packet trace capture. To
retrieve the entire data provenance using this approach, it is essential to
sequentially access the storage space of each node along the routing path.
Hence, the base station cannot independently decompose the complete provenance
of each data packet. Liu and Wu (Liu and Wu, 2020) introduced an algorithm for
compressing provenance called index-based provenance compression. To reduce
the overall size of the provenance data, their approach combines the concept
of typical substring matching with path identifier and path index to represent
path information within data provenance. Additionally, they expand the data
provenance scheme to include attack detection and present a method for
identifying malicious nodes based on this expanded scheme. The proposed scheme
falls short in terms of ensuring data integrity, lacks a thorough security
analysis within a defined threat model and results in computationally
intensive operations. Additionally, Tang and Keoh (Tang and Keoh, 2020)
present a methodology to ensure the unaltered reporting of energy usage data
of home appliances. The proposed approach guarantees the collection of data
from the correct and trusted source at the specified location. This framework
is specifically designed for Home Area Networks (HAN) within a smart metering
infrastructure, with the primary objective of confirming the authenticity of
data, source identity, and location. The MAC is used to verify data
authenticity and integrity. It should be noted that the authors assume the
trustworthiness of the receiver (i.e. smart plug), while acknowledging the
possible vulnerability of one of the two senders (i.e. smart plug or magnetic
sensor) to attacks. To achieve location authenticity verification, the system
integrates a third sender (i.e. a Bluetooth device), which operates
independently and does not collude with other system components.

In their work, Porkodi and Kesavaraja (Porkodi and Kesavaraja, 2021) present a
framework to secure the data provenance by introducing blockchain and access
control policies in IoT systems. They use hybrid attribute-based encryption to
securely transmit provenance information. The proposed method is evaluated
using different performance metrics such as computational cost and the
throughput of encryption/decryption, and the key strength is calculated using
the effect of avalanche. Additionally, the authors conducted experiments to
prove that the proposed approach reduces computational cost and achieves high
throughput. In their research, Xu et al. (2022) introduce two provenance
schemes for Wireless Sensor Networks. The first is the Path Index Differences-
based Provenance (PIDP) scheme, where provenance information is encoded using
differences in packet path indices. Specifically, it links an index
representing the packet path’s similarity to the main backbone path and the
variation between the actual packet path and this backbone path. Additionally,
the authors propose a second scheme known as the Path Hash Value-based
Provenance (PHP) scheme. In this approach, provenance data is encoded as a
combination of the data source node’s ID and a part of the hash value taken
from the packet’s path. Both schemes are evaluated based on their provenance
compression rate and energy conservation rate, and these metrics are then
compared to those of the DP scheme. Xu and Wang (Xu and Wang, 2022) introduce
a provenance scheme called Multi granularity Graphs-based Stepwise Refinement
Provenance (MSRP). In this scheme, they utilize mutual information between
pairs of nodes as a similarity index to categorize node IDs. This
categorization forms the basis for generating multi granularity topology
graphs. Additionally, they apply the Dictionary-based Provenance (DP) scheme
for stepwise encoding of the provenance information. The Base Station (BS)
follows the same stepwise approach for provenance recovery and simultaneously
conducts data trustworthiness evaluation during decoding. The performance of
the MSRP scheme is thoroughly evaluated through a combination of simulations
and testbed experiments. Results show that the scheme achieve high provenance
compression rate, energy utilization and efficiency of data trustworthiness
assessment.

5.2 Bloom Filters — A compact data structure, using hashing, enables rapid
verification of item presence within the structure. Provenance information is
embedded within the generated structure and the bloom filter is transmitted
along with the data. By employing this method, the original information
remains inaccessible to potential adversaries. Due to the variability of the
bloom filter value from one packet to another, establishing a connection or
association between previous bloom filters and recent data becomes
challenging. In their work, Sultana et al. (2015) present a secure provenance
scheme for WSN. Their approach involves embedding provenance information into
a Bloom filter (BF), which is transmitted alongside the data. This scheme
effectively addresses the challenges posed by resource constraints in WSN. It
requires a single channel for data and provenance transmission. The scheme
overlooks data integrity and only focuses on studying the packet drop attack.
Additionally, Siddiqui et al. (2019) present a data provenance technique for
IoT devices that employs Bloom Filter and attribute-based encryption. This
approach presents challenges as IoT devices typically have limited memory
capacity, making it impractical to store extensive provenance information.
Furthermore, the technique is vulnerable to physical attacks, whereby an
attacker can easily manipulate the stored provenance information within an IoT
device.

Harshan et al. (2020) introduce a method for embedding provenance information
known as the Deterministic Double-Edge (DDE) embedding mechanism, which is
based on Bloom filters (Lu et al., 2012; Shebaro et al., 2012). In this
approach, a relay node embeds information about both edges connected to it:
the edge through which it receives a packet and the one through which it sends
the packet. This means that a node can effectively cover two edges in the path
with a single action, reducing the need for the next node in the path to
update the provenance. This results in decreased packet delay. Authors show
that the hop-counter within the provenance segment can also be utilized to
coordinate the skipping strategy among nodes. Additionally, they propose upper
bounds on the error rates of the DDE embedding technique, which are expressed
as functions of the network’s node count, the number of hops, the size of the
Bloom filter, and the number of hash functions computed by each node.

5.3 Fingerprints — Fingerprints refers to unique identifiers or signatures
assigned to the communication links between IoT devices. These fingerprints
are typically generated based on various characteristics or attributes of the
link, such as received signal strength, latency, packet loss, or other network
metrics. The homomorphic feature of public-key cryptography is exploited by
numerous anonymous fingerprinting systems. These approaches enable the user’s
fingerprint to be embedded in an encrypted domain (using public key) so that
only the user may access the decrypted fingerprinted material by using the
private key (Megías, 2015). Data provenance solutions have been developed by
analyzing and comparing link fingerprints generated from different attributes.
In their work, Ali et al. (2014) present a method for enhancing the security
of data provenance in bodyworn medical sensor devices. They achieve this by
using the spatio-temporal characteristics of the wireless channels used for
communication by these devices. This solution allows two parties to create
highly similar link fingerprints, which uniquely link a data session to a
specific wireless connection. This link enables a third party to later verify
transaction details, especially the specific wireless link through which the
data was transmitted. To validate this approach, experimental testing was
conducted using MicaZ motes running TinyOS, and the results show an
improvement in energy efficiency. Additionally, the proposed technique
generates provenance information on each session, reducing the use of
cryptographic methods. Using different fingerprint method, Alam and Fahmy
(Alam and Fahmy, 2014) propose a provenance encoding and construction method
that adapts three encoding schemes: juxtaposition of ranks, prime scheme, and
Rabin fingerprints. The method is referred to as Probabilistic Packet Flow
(PPF). In the first scheme, provenance is constructed based on the rank of the
node instead of the node ID, which requires fewer bits for encoding. Assuming
that the packet contains a specific space to hold the identities of $m$ nodes,
a counter of $\log(m)$ bits is used to track the embedded ranks in the packet.
The second scheme is based on prime multiplication, which embeds more node IDs
using the same number of bits compared to the previous one. This method uses
prime numbers as node IDs, and their multiplication results in encoding a set
of IDs that can be uniquely factorized. In their third method, a data path
traversed by a packet is a sequence of bits that represents the IDs of nodes
on that path. The fingerprint of this sequence of bits is transmitted instead
of the actual sequence. Kamal and Tariq (2018) introduce a lightweight
protocol for a multi-hop IoT network, aiming to ensure both data security and
the establishment of data provenance. The protocol utilizes link fingerprints
derived from the RSSI of IoT nodes within the network. The protocol achieves
data provenance by appending the encoded link fingerprint to the data packet
header as it traverses each node. After receiving the packet, the server
decode the packet header in a sequential process. However, provenance data
expands rapidly, which requires transmitting a large amount of provenance
information to data packets, thereby increasing the bandwidth overheads.

5.4 Blockchain-based Solutions — Distributed ledger technologies, like
blockchain, have gained visibility for maintaining the security and privacy of
provenance data. These blockchain platforms serve as the primary means to
ensure data integrity by creating transactions as provenance records that are
chained in a secure manner. Once recorded on the blockchain, these records
cannot be altered or removed, thereby establishing a trustworthy system for
verifying the integrity of the provenance information in a network environment
that is vulnerable to different attacks.

Baracaldo et al. (2017) propose a framework in IoT environments to maintain
and secure IoT provenance data, whose main features are: (i) use of a
completely distributed lightweight keyless blockchain element (i.e. Keyless
Signature Infrastructure Module (KSI) (Buldas et al., 2013, 2014)) to
guarantee that the integrity of the provenance data is protected; (ii)
maintenance of provenance information confidentiality by granting specific
access controls to various parties as required; and (iii) high level of
availability of provenance. In addition, provenance data in (Baracaldo et al.,
2017) can be subject to restricted access policies. The integration of
cryptographic algorithms in the solution ensures confidentiality since it is
difficult to manage the flow of data and its provenance in IoT contexts.

Zeng et al. (2018) propose a blockchain-based data provenance scheme (BCP)
which deploys a distributed blockchain database that is connected to the
sensor network through edge computing. In this scheme, each node updates the
provenance records along the packet data path. These records are obtained by
high performance nodes (H-nodes), which are edge computing nodes placed either
above or close to the WSN, through the process of packet sniffing (Shi et al.,
2016). Then, the base station retrieves the provenance records by querying the
H-nodes. Moreover, provenance records are stored in a blockchain-chain
database after encryption through invoking a smart contract running on
Ethereum Virtual Machine (EVM) (Wood, 2014). In this model, each provenance
record contains node ID, sequence number, hop count, sequence numbers of the
aggregated packets, and the number of times a packet is aggregated.

In their work, Javaid et al. (2018) propose a solution for data integrity and
data provenance in IoT networks by using physical unclonable functions (PUFs)
and a blockchain variant with smart contracts that is Ethereum. This method is
called BlockPro. PUFs are used to maintain data provenance by providing unique
hardware fingerprints. To overcome data tampering attacks and block
unregistered devices, Ethereum is used as a decentralized digital ledger. With
the presence of expanding sequence of records, data undergoes initial
validation before being permanently stored on the blockchain. Once stored, it
can not be tampered with or modified, thus ensuring data integrity. Sigwart et
al. (2020) implement an IoT data provenance framework based on smart contracts
using a generic data model to provide data provenance capturing, storing, and
querying functionalities for different IoT use cases. This work extends their
approach proposed in (Sigwart et al., 2019). The framework uses the data
provenance model by Olufowobi et al. (2017). The authors conduct an assessment
of the proposed framework with specified requirements through the
implementation of a proof-of-concept using Ethereum smart contracts. In this
approach, a provenance record ($prov(dp)$) for a data point ($dp$) is composed
of a 3-part structure. It links the address ($addr(dp)$) of $dp$ (i.e.
essentially an identifier ID) with the set of provenance records associated
with the data points that are used to create a $dp$ (referred to as
$inputs(dp)$), and includes a context element ($context(dp)$). This element
includes information for provenance purposes, such as details about the agents
involved in the computation of the data point, timestamp, location, or the
specific execution context within the IoT system.

According to Liu et al. (2020), a multilayer provenance query index and a
blockchain-based architecture are proposed for the network provenance in the
IoT: blockchain-based secure and efficient distributed network provenance
(SEDNP). For effective representations in the Verifiable Computation (VC)
framework, the design integrates range, keyword, and K-hop ancestor queries
into a single model. The authors also create a digest hashing method that
verifies the provenance log and index. With constant-size digests, they
decrease the storage and processing costs associated with on-chain
transactions regardless of the volume of data. Along with extensive security
research, they formalize preserving security to record the requirements for
the validity and integrity of the query results. Finally, using a proof-of-
concept approach that combines the Pinocchio VC framework and a testing
blockchain network, the authors analyze the implementation issues. For cloud-
based IoT networks, Siddiqui et al. (2020) provide an application layer data
provenance system that relies on an execute-order architecture. By using
outsourced encryption on Edge nodes using Ciphertext-Policy Attribute-based
Encryption (CP-ABE), it allows fast transaction throughput on the blockchain
network with minimal security overhead. The workload on the IoT nodes is
reduced since every communication between IoT devices is connected to a
blockchain network and recorded on authorized blockchain peers. Smart
contracts are used to store the provenance data on the blockchain. The IoT
Device Registration smart contract begins when an IoT node connects to the
network. The Data Transfer smart contract is run whenever a message is
transmitted from one IoT node to another. This is responsible for preserving
the provenance information on the blockchain. The Provenance Verification
smart contract, which is in charge of confirming the provenance data, is then
run when it has been confirmed that the message was transmitted by the
appropriate IoT node.

Another solution is presented by Yin and Fu (2022), which use blockchain and
smart contract to develop a data provenance scheme for IoT applications. The
scheme includes a smart contract on the blockchain with reasonable access
control policy. The access authorization for users and data sources is limited
to maintain security of generated data. For data security and data integrity,
the suggested approach implements two data structures: provenance record and
provenance record set. To achieve data provenance, blockchain’s non-
repudiation is used. Access to the data is made secure by this structure. The
provenance system can continue to function and the smart contract may impose
the data owner’s policy. To evaluate the feasibility of the proposed scheme,
Ethereum network test is used for verification. Additionally, Sun et al.
(2022) construct a blockchain-based IoT data provenance model through adapting
the PROV data model (PROV-DM) (Missier et al., 2013) to address the issue of
recording provenance data generated by a multi-layer IoT system. The
provenance elements are defined by PROV-DM along with their connections. The
W3C provenance family of standards has this conceptual data model as its
basis. To create an IoT data provenance model, it is therefore required to
improve and expand the PROV-DM. In this work, authors present the needed
requirements to design and build a robust data provenance model. Then, they
propose a model based on PROV-DM using a blockchain network to secure
provenance records from being tampered. To demonstrate how the suggested
approach may be used to build a provenance graph and identify which agent is
operating IoT data abnormally, they provide an application scenario in the IoT
trustworthy data sharing system.

ProvNet, a distributed data sharing system that may ensure data ownership and
accurately record and preserve data sharing provenance, is proposed by Chenli
et al. (2022). Users can share data in two ways: through the same service
provider, where provenance verification and storage will be handled by the
service provider and certain users; or through other service providers, who
will work together to maintain a provenance graph and authenticate sharing
records. They suggest a blockchain variant structure that can provide both
forward and backward tracking in order to store the provenance information
during the sharing process. A directed graph is appropriate for keeping the
provenance records because of the nature of data sharing, which allows senders
to re-share the datasets they receive and allows datasets to be shared among
many recipients simultaneously. ProvNet suggests storing the provenance
records in a networked blockchain, or blocknet, as an alternative to a single
blockchain. In order to perform forward tracking, ProvNet selects redactable
blocks (Ateniese et al., 2017; Brzuska et al., 2009) and Chameleon Hash
(Krawczyk and Rabin, 1998; Camenisch et al., 2017), allowing a block to record
the hash value of its subsequent block while maintaining its own hash value.

5.5 Watermarking and Physical Unclonable Functions — Watermarking is a widely
known advancement in the field of WSN security. It serves the purpose of
identifying any alterations made to sensory data, effectively preventing
unauthorized interception (Megías et al., 2010; Araghi et al., 2023). The two
main categories of digital watermarking are fragile watermarking and robust
watermarking, each offering different anti-attack properties. Fragile
watermarks become undetectable when data is modified, whereas robust
watermarks can withstand various forms of distortion (Cox et al., 2007;
Megías, 2021). Watermarking is being used in many security applications and is
introduced in some proposed data provenance works. Sultana et al. (2011)
develop a technique for data provenance aimed at identifying malicious packet
dropping attacks. The technique depends on the timing characteristics between
packets after the process of embedding provenance information. Based on the
distribution of inter-packet delays it detects the packet loss. Then, it
identifies the presence of an attack and localizes the malicious node or link.

Similarly, Physical Unclonable Functions (PUFs) provide a hardware-oriented
system that generates a response for a specific challenge, ensuring uniqueness
for each device in the system. PUFs facilitate the authentication of IoT
devices that generate sensitive data while maintaining device anonymity.
Through the presence of a trusted third-party verifier, the identities of
these devices can be verified without compromising their anonymity (Kanuparthi
et al., 2013). Aman et al. (2019) developed two secure protocols for data
provenance in IoT networks, aiming to achieve authentication and privacy
preservation. These protocols address two different scenarios: the first
scenario involves a direct connection between an IoT device and a wireless
gateway, while the second scenario deals with IoT devices indirectly connected
to the wireless gateway through multiple hops of other IoT devices. Both
protocols use PUFs in addition to wireless link fingerprints generated from
the RSSI between communicating nodes. Through experimental analysis, the
authors show that these protocols achieve high efficiency in terms of
computational complexity and energy utilization, while also obtaining
robustness against various physical and cloning attacks. Aman et al. (2021)
propose an analytical model to create a mechanism that enables the
establishment of data provenance in IoT systems. Their approach incorporates
PUFs and the extraction of fingerprints from the wireless channel, along with
the implementation of mutual authentication and anonymity measures, all aimed
at achieving robust data provenance. The approach lacks consideration for the
multi-hop scenario and fails to adequately address tracking of data packet
provenance. Moreover, Hamadeh and Tyagi (Hamadeh and Tyagi, 2021) propose an
approach which combine two solutions: data provenance and privacy prserving in
IoT networks. They provide trustworthiness and dependable IoT networks by
using PUFs with non-interactive zero knowledge proof. In this method, an IoT
device has the capability to transmit data to its respective server without
revealing its identity, as it provides proof of ownership. In particular, the
method under consideration is designed to validate that the authorized device
executed an authorized program for creating or modifying data. Authors
introduce a privacy-centric data provenance protocol. To validate practicality
and effieciency, they developed the protocol using Altera Quartus and
subsequently implemented it on an Altera Cyclone IV FPGA.

5.6 Graph-based Provenance — Graph-based provenance is a method used in data
provenance encoding to represent the origin or history of data. These graphs
capture the relationships and dependencies among data elements, processes, and
transformations, providing a visual or structural representation of how data
has evolved over time. Three different types of graph-based provenance
representations as descibed below.

_Event-flow graphs_ are a type of provenance graph that represents data
provenance by capturing events and the flow of data between them. Each event
corresponds to a data operation or transformation, and edges in the graph
indicate the data flow from one event to another. Chang et al. (2022) propose
a data provenance approach for provenance systems in IoT applications based on
event-action flows instead of provenance graphs. Even-action flow is defined
as a sequence of events and actions including a time-stamp in an execution
trace. They are less complex than provenance graphs due their simple structure
form which allows users to understand it easier. In their work, the authors
present an event-flow graph to regular users as a static abstraction of every
possible provenance graph for IoT applications. They dynamically link time-
stamped events and actions to statically create event and action nodes. Then,
users can query provenance information from the event-flow graph by selecting
an event or action node to choose the associated time-stamped actions or
events. After users create a query by picking a timestamped event or action,
the system will respond with one of two types of information: “What
provenance” answers questions about which events or actions are triggered by
the user’s specified action or event, and “Why provenance” provides insights
into the events or actions responsible for causing the user-specified action
or event. The system is developed as a Graphical User Interface (GUI) forming
a user-friendly graphical provenance system.

_Provenance graphs_ include a variety of graph structures that capture
provenance information. These graphs may use different notations and
structures to represent data history, depending on the specific needs of the
application or research. In a provenance graph, nodes represent data entities,
processes, or events, and edges denote relationships, transformations, or
dependencies between these nodes. Jaigirdar et al. (2023) extend their work,
Prov-IoT (Jaigirdar et al., 2020), to provide security information for
provenance graphs. Prov-IoT is a security-aware IoT provenance model. In this
work, security metadata is integrated with specified security policies within
the provenance graphs. They propose an IoT-Health scenario with a number of
potential threats: fault packet injection, node cloning, unauthorized access,
malicious code injection and denial of service. Three major node types are
used to describe the scenario: agent, entity, and activity (Moreau et al.,
2011). The terms Was Associated With (WAsW), Was Informed By (WInB), Was
Derived From (WDeF), and Was Generated By (WGeB) are used to represent the
relationships between these three nodes. A general provenance graph for the
IoT-Health scenario is generated using the W3C-standardized PROV-DM concept.
To evaluate the system and check for potential risks, the approach is
evaluated based on six cases: permission violation, missing Web Application
Firewall (WAF), intrusion detection, unauthorized access, denial of service,
and identifying failed signature verification.

Finally, the use of _Directed Acyclic Graphs_ (DAG) allows directed structures
with no cycles. This means that data transformations and dependencies are
represented as a directed flow without loops or feedback. DAGs are used to
represent provenance information because they are well-structured and provide
efficient querying and tracking of provenance records. ProChain framework is a
provenance-aware approach of traceability proposed by Al-Rakhami and Al-
Mashari (Al-Rakhami and Al-Mashari, 2022) for IoT-based supply chain systems.
The IOTA protocol, a third-generation DLT, is utilized by the ProChain
framework. To overcome limitations and provide a scalable, quantum-resistant,
and attack-proof solution for the systems built around IoT, it makes use of
the DAG information structure in contrast to the linear structure used by the
blockchain (Silvano and Marcelino, 2020). ProChain enables food item
traceability from manufacturing to retailer with the use of several IoT
sensors and provenance data at each engaged supply chain phase. ProChain
strengthens and improves the management and optimization of all operations
while also serving as a guarantee for the quality and safety of food. On the
Raspberry Pi 3B platform, the ProChain idea is evaluated by simulating an IoT-
deployed supply chain. The average measured time and energy consumption were
then evaluated to check on the usability of the framework. The authors review
and implement the framework to show how it may be used in supply chain systems
to couple supply chain data with the IOTA Tangle and generate provenance
information by adding data for various payload sizes.

5.7 Sanitization and Lexical Chaining — There are situations where privacy
concerns prevent some information from being shared inside the provenance
chain. This is where the method of data sanitization is presented. Data
sanitization assists in hiding some important information which could
compromise privacy. Data sanitization is mainly used to make sure that data is
consistent, accurate, and dependable, such that it can be used for reporting,
analysis, and data-hiding for privacy preservation. Lomotey et al. (2019)
present two approaches for device and data verification in an IoT network to
achieve trust and privacy preservation. The first approach enables devices to
subscribe and reveal their metadata to allow for data packet tracing without
compromising privacy. To achieve this, a data sanitization method for hiding
sensitive information is applied. Users and devices have the option to label
attributes within the provenance data as non-shareable with other devices in
the chain. In the second approach, the authors modeled the entire peer-to-peer
IoT network as a graph network. Data origins are verified using Floyd’s
algorithm between different interconnected nodes. This work ensures
transparency, traceability, and privacy preservation through both proposed
approaches.

Semantically similar words or phrases are linked together by lexical
cohesiveness. The related elements can be linked together to create Lexical
Chains after all cohesive relationships have been determined forming a
conceptually accurate building blocks in a variety of natural language
processing systems (Remus and Biemann, 2013). This is how a typical chain may
be written:

$\text{Device}\rightarrow\text{Type}\rightarrow\text{Owner}\rightarrow
Communicated$

would be feasible to track and categorize the communication channels between
different IoT devices as well as their previous interactions using this
method, which may be used for machine-to-machine communication. In their work,
Lomotey et al. (2018) first emphasize the use of provenance through the design
of algorithms to confirm the origin of IoT-based data, and then they propose
developing completeness techniques through visual analytics to trace data
packets through the complete route in an IoT network. They present an improved
system provenance mechanism in their study to achieve traceability. The
technique is based on an associative logic to decide all connections
established in a machine-to-machine communication between IoT nodes. To create
links between device communication and data propagation to the $n$-th degree,
a statistical lexical chaining based on the Adjusted Rand Index (ARI) is
suggested as an alternative to knowledge-based methodology. Because data
propagation pathways and object-to-object communications can be identified,
the proposed IoT architecture makes traceability easier. Based on their
findings, the suggested system demonstrates that, in terms of identifying
linkability, unlinkability, and availability, the ARI is more accurate than
the knowledge-based methodology. Additionally, visual analytics is offered to
give a clearer understanding of interconnection in IoT nodes using
visualization graphs such as HyperTree Graph, the Weighted Graph, and the
combination of SpaceTree and RGraphs.

5.8 Path Difference — Path difference is the sum of an indicator variable
which represent whether the actual packet path for the next hop is the same as
the parent node of this packet along the routing path. Parent information can
be used in reference packets for path reconstruction if the path difference
value is equal to zero. If not, more time will be required to record the real
forwarders. In order to minimize the message overhead, it is better to have a
minimal path difference value. In big-scale sensor networks having lossy links
and complex routing dynamics, Gao et al. (2013) mention the ineffectiveness of
current path reconstruction techniques. The authors present Pathfinder, a
cutting-edge path reconstruction method. Pathfinder takes advantage of
temporal correlation between a group of packet paths at the node side and
effectively compresses the route information with path variation; at the PC
side, Pathfinder determines packet routes from the compressed data and uses
smart path speculation to reassemble the data packet paths with a high
reconstruction ratio. With the use of comprehensive simulations and traces
from a large real-world sensor network, they construct Pathfinder and evaluate
its performance against the two most similar approaches. Results indicate that
Pathfinder performs noticeably better than MNT (Keller et al., 2012) and
PathZip (Lu et al., 2012) in a number of network setups. Their findings show
that Pathfinder achieve both low transmission overhead and high reconstruction
ratio.

5.9 Logging-based Techniques — Concerns over creating appropriate forensic
investigation models were highlighted by cybercrime occurrences seen in IoT
networks. Every attack on an IoT network leaves behind some evidence of it,
but the primary difficulty is locating, gathering, and correlating that
evidence for accurate forensic analysis. In order to provide answers to
specific investigation queries, it can sometimes be rather challenging to
determine the linkages between discrete information gathered from IoT devices
and network level activity. To tackle this issue, forensic investigators can
benefit from provenance logging. Over time, network provenance generates a
vast amount of information. IoT devices reduce this cost by using template
systems, which require them to log just basic network characteristics as the
traditional device logging.

Sadineni et al. (2023) propose a template-based provenance approach, ProvLink-
IoT, to provide reliable forensic analysis in IoT networks. ProvLink-IoT is
developed to analyze link-layer attacks. Many open source tools are used to
implement the system in a simulated environment to approve its robustness in
correlating evidence that is found in link-layer provenance. Based on
provenance logs and network data gathered from the network, traceability
graphs are generated in both normal and attack scenarios. The approach is
studied using 6TiSCH network stack. To analyze the effects and conduct
forensic analysis, the authors applied three link-layer attacks to the Time
Slotted Channel Hopping (TSCH) and 6top (i.e. operational sublayer) layers of
the 6TiSCH network. For performance evaluation, they used storage overhead and
provenance growth rate.

5.10 Frameworks using Storing Methods — Researchers have used various storage
methods, using database placement strategies, to develop frameworks designed
for diverse IoT applications. These methods include a number of techniques for
storing and querying provenance information within the database. They offer
management solutions to ensure the secure storage of provenance data while
meeting security requirements. These frameworks offer a complete cycle for
tracking the history of provenance records, covering everything from initial
capture to storage and analysis. In this section, we present selected works
that have designed such frameworks for managing data provenance.

Alkhalil et al. (2019) introduce a bio-inspired approach that uses the
processes of human thinking to enhance data provenance in WSN. Their proposed
Think-and-Share Optimization (TaSO) algorithm modularizes and automates data
provenance management in enterprise-deployed WSN. The TaSO algorithm is
designed of four phases: Think, Pair, Share, and Evaluate. The authors assess
the effectiveness of their TaSO algorithm by evaluating key metrics such as
connectivity percentage, closeness to the sink node, coverage, and running
time.

Wang et al. (2018) introduce the ProvThings framework, designed for the
capture, administration, and analysis of data provenance within IoT platforms.
Their approach introduces a selective instrumentation algorithm that reduces
the collection of provenance data by identifying security-sensitive sources
and sinks. This method provides a means to trace complex chains of inter
dependencies among IoT components. Additionally, the authors created a
prototype of ProvThings for the Samsung SmartThings platform and evaluated its
effectiveness against 26 IoT known attacks. The IoT provenance model is based
on the W3C PROV-DM (Groth and Moreau, 2013). The results indicate that
ProvThings imposes only a 5% overhead on physical IoT devices while enabling
real-time querying of system behaviors.

Finally, Elkhodr and Alsinglawi (2020) extends previous work that introduced a
provenance-based trust management solution to assure data provenance (Elkhodr
et al., 2016). Their Internet of Things Management Platform (IoT-MP) (Elkhodr
et al., 2016) establishes trust relationships between communicating IoT
devices. This work uses the existing capabilities of IoT-MP to enhance privacy
protection in IoT networks. Furthermore, it introduces a Data Provenance
module aimed at enabling the retrieval of data origins and access to device’s
history, including the networks it has interacted with. The method propose
three states for IoT devices registered in the IoT-MP platform: New Resident,
Visitor, and Returning Resident. Additionally, it includes a database module
that is reconstructed to adapt to the changes in the architecture.

After analyzing the selected state-of-the-art papers based on our proposed
taxonomy in Figure 3, the findings of this analysis is shown in Tables 4 to 8
in Appendix A. In Table 4, each paper is analyzed based on the provenance
encoding method, provenance storage method, application, security analysis,
advantages and shortcomings. We present the main performance metrics that are
needed to evaluate any data provenance approach in IoT networks that are
provenance length, energy comparison, data packet size, link-loss rate,
detection rate, false positive rate, false negative rate and computation time.
We take into consideration for the analysis of each selected approach these
performance metrics which are presented in Table 6. Moreover, in Table 5, we
provide details on the provenance category, implementation, and simulation
software. This resource is presented to help researchers in exploring the
different implementation tools and simulation software associated with each
provenance category. Table 7 summarizes the security requirements for data
provenance integration in IoT networks, previously introduced in Section 3.
Similarly, Table 8 summarizes the security attacks illustrated in Figure 8.

##  6\. Discussion

Data provenance can be used to detect errors in the different stages of data
generation and processing enabling the system to detect the nodes that
produced those errors (Yu et al., 2017; Yan et al., 2017; Yu et al., 2019). In
addition, storing detailed information about data in the provenance record
allows for data recovery when data is no more usable to ensure availability
and achieve normal data communication within different network entities
(Foster, 2003). Data provenance enhances data readability when it includes
detailed information about the data’s origin and processing (Jagadish and
Olken, 2004). Furthermore, data provenance enhances data clarity, ensure data
reliability, and facilitates data reuse (Jensen et al., 2013). One of the most
important features of data provenance is providing the system with the ability
to asses the trustworthiness of generated data through different secure
provenance techniques (Sabaa and Panda, 2007). However, the size of provenance
records depends on the number of nodes involved in generating the provenance
information and the quantity of attributes to be included in each record. To
obtain a provenance chain that satisfies security requirements, it is
essential to include many attributes that describes the origin,
transformation, data path, data quality and any modifications the data has
undergone. In large-scale networks with an increasing number of nodes, the
size of provenance information grows rapidly, posing significant challenges in
terms of storing and querying these records. This can limit the efficiency of
provenance analysis. There should be a trade-off between the number of
attributes included in the records and the limitations in the computational
capabilities of the system. This requires to determine the most important
security requirements of the system and the needed attributes that satisfies
it with minimum storage and querying overhead.

The presence of constrained network components and limited resources in IoT
environments presents several challenges for data provenance schemes. Based on
the analyzed literature, any data provenance scheme designed for such
environments must address multiple challenges to be effective (Sultana et al.,
2013; Alkhalil and Ramadan, 2017; Suhail et al., 2016). One of the important
challenges is minimizing bandwidth consumption while ensuring high data
processing and throughput in the provenance infrastructure. Properly indexing
the provenance records is also essential due to the extensive nature of
complete provenance. Queries often involve more complex operations than simple
name-based databases, requiring users to search for specific data sets based
on subsets of attributes and values within the provenance chain. Different
users may query different attributes depending on their objectives,
necessitating efficient indexing structures for databases across various
dimensions. Additionally, managing the size of provenance data efficiently is
crucial, as provenance records in large-scale systems typically exceed the
size of the actual data when processed and transmitted.

Establishing secure transmission of provenance information and enabling the
detection of malicious attacks with fast response from provenance management
systems is vital. Efficient storage of provenance data is equally important,
particularly as original data undergoes multiple hops and accumulates complex
processing histories, resulting in large provenance information. There should
also be flexibility in querying provenance data to allow reconstruction when
an authorized entity queries it from the provenance storage entity. Collecting
provenance information poses its own challenges, as different data features
may be collected and stored based on the service and requirements of the IoT
system. Often, the system requires the collection of various operations or
information about forwarding entities, necessitating effective handling of
these records from the generation of data to its final receiving node.

##  7\. Open issues and research challenges

In order to open up additional discussion and suggest recommendations for
enhancing the development of data provenance in the IoT networks, we have
outlined several open problems, research gaps, challenges and limitations from
the studied literature.

7.1 Open issues — First, we identify some representative open issues and
potential research directions which can help in exploring different aspects of
data provenance integration in IoT networks.

  * •

Privacy and security of data stored on the blockchain: Integrating a
blockchain with the IoT network provides a robust database that provides
integrity to the provenance records stored as transactions. These records
include sensitive information about the creation, processing and transmission
of the data packets. Hence, it is essential to preserve privacy while
maintaining the integrity of the complete provenance chain. Attackers may try
to obtain secret information by analyzing the provenance chain which the
blockchain holds. It is important to secure the records stored on the
blockchain.

  * •

Range of studied attacks: Most of the proposed approaches focus on a very few
and limited number of attacks. In many cases, researchers take into account
either data attacks or provenance attacks. Attackers aim to deceive the system
by targeting both data and provenance records. The majority of existing works
focus on the forgery and modification attacks, which are the most common
threats. It is clear, from Table 8, that almost all the proposed methods do
not consider chain tampering. As mentioned above, a complete provenance
information of a particular data packet is created by connecting provenance
records in a chain. The objective of an attacker is to change this chain’s
sequence and contents to affect the accuracy and dependability of the
provenance data. Hence, to develop any solution for provenance in IoT, the
robustness of the system against data and provenance attacks must be taken
into account.

  * •

Watermarking as a solution: Maintaining data integrity through procedural
requirements is the major goal of watermarking system design. These
requirements cover node ownership, data integrity, and bandwidth
establishment. Embedding a watermark with data is the main technique to
achieve the requirements in watermarking schemes (Sultana et al., 2013, 2015;
Dai et al., 2008; Hussain et al., 2014; Panah et al., 2015). The criteria used
in the generation of watermarking schemes is not restricted to WSN; it also
includes the security of multimedia programs (Sultana et al., 2011; Hasan et
al., 2009b; Prasad and Koliwad, 2010; Fallahpour and Megías, 2015) and
database formation (Jain and Jain, 2015; Gosavi and Mali, 2015; Khan et al.,
2013). This wide range of domains allows watermarking to be one of the
lightweight solutions in limited networks such as IoT. Hence, the features of
data hiding that watermarking provides a new provenance encoding technique to
store provenance records in an efficient way in-terms of storage and
transmission overhead. Only one relevant work (Sultana et al., 2011)
considered watermarking for data provenance. They consider inter-packet timing
characteristics for embedding provenance information and does not use the data
features and watermarking embedding with the data packets. This technique can
be used to develop lightweight solutions for the limitations of IoT networks
in terms of storage and computation.

  * •

Lack of complete provenance management system and narrow focus: The literature
is still missing a robust provenance management system that takes into account
all required procedures from collecting, storing and analyzing provenance
information. The system should satisfy the security requirements while
maintaining storage and data management issues. To achieve this, these systems
should be designed based on the architecture of IoT networks, taking into
account various factors, such as communication protocols, computational
capabilities, storage limitations, node coordination, database integration,
and potential security attacks against data and provenance records.
Additionally, it is essential to thoroughly test the robustness of the
proposed provenance system, assessing its performance across all relevant
metrics. This approach ensures that the system complies with all the necessary
objectives, rather than evaluating its performance based on only a subset of
criteria. Many of the existing approaches fail to adequately address these
important issues while maintaining the security of provenance data.
Furthermore, the proposed provenance approaches focus on a specific
application for a specific domain. The fast development of interconnected
applications of different domains require adapting these techniques to be
applied to multiple domains that include different structure of provenance
records.

  * •

Lack of efficient query and provenance support: In IoT networks, which involve
numerous interconnected devices generating huge amounts of data, efficiently
querying and managing the history and transformations of this data is a
complex task. The absence of effective mechanisms for querying and handling
provenance can hinder the ability to trace the origin and processing of data,
resulting in a limitation in providing security, freshness, and scalability in
IoT applications. Addressing this issue involves developing more efficient and
scalable methods for querying and managing provenance data in the unique and
dynamic environment of IoT networks.

  * •

The storage of blockchain is expensive: Blockchain relies on a decentralized
and distributed network of nodes, each maintaining a copy of the entire
blockchain. While this redundancy enhances security and fault tolerance, it
significantly increases the storage requirements. In IoT networks, with a
large number of devices generating data, the continuous increase in the size
of the blockchain can grow rapidly. Additionally, achieving scalability in
blockchain networks, especially in public networks, is a known challenge. As
the number of transactions and participants increases, maintaining the
performance and efficiency of the blockchain becomes a complex task, hence
requiring more robust infrastructure and resulting in an increased operational
costs. In order to overcome the issue of expensive storage in blockchain-based
data provenance for IoT networks, researchers should develop a more scalable
and efficient validation mechanisms, optimization of data storage techniques,
and alternative blockchain architectures in IoT that balance the advantages of
security with the need for cost-effective scalability in the context of IoT
data provenance.

  * •

Integrating data provenance with Intrusion Detection System (IDS): Adding data
provenance to system logs transforms them from simple records into rich
sources of security insights which are used in traditional Host-based
Intrusion Detection Systems (HIDS). This helps investigators to detect
security threats with greater accuracy, minimizing computational time and
resources on false alarms, as extensive research has documented (Ayoade et
al., 2020; Barre et al., 2019; Berrada and Cheney, 2019; Xie et al., 2016,
2020; Han et al., 2017; Du et al., 2017; Gao et al., 2018b; Xie et al., 2021;
Berrada et al., 2020; Gao et al., 2018a; Garchery and Granitzer, 2020; Hassan
et al., 2020; Wang et al., 2020). Data provenance can be integrated with HIDS
to improve intrusion detection. IDS based on provenance, known as Provenance-
based Intrusion Detection Systems (PIDS), leverage data provenance to identify
intrusions. This involves examining not only the properties of system entities
but also dealing with the causal relationships and information flow within a
provenance graph (Zipperle et al., 2022). However, the integration of data
provenance and IDS in IoT networks has not been widely studied. This opens a
crucial field for researchers to study, addressing the significance of
integrating data provenance in IoT networks and exploring its various
applications for enhancing intrusion detection.

7.2 Research challenges — The integration of data provenance with IoT raises
critical security concerns and in this section we summarize some of the
important challenges observed while integrating provenance with IoT.

A first challenge deals with _provenance records processing and storage_.
Indeed, provenance information may be larger than the data itself, since
provenance records gets larger as the number of forwarding nodes increase.
While some applications may involve a small-scale network, there is a need for
a complete provenance representation in the provenance chain. This
representation requires a number of attributes that need to be stored in the
provenance records which is also an issue with storage. Additionally, the
diversity of IoT devices and sensors leads to a variety of data types and
formats.

Provenance records must also accommodate to the different types, leading to a
larger and more complex datasets. Also, to provide useful information from the
history and processing of data, provenance records need to be detailed and
granular. This granularity adds to the size of the provenance dataset,
especially when capturing detailed information about each data transformation.
In large-scale IoT deployments, devices are often interconnected in complex
networks. Provenance records need to traverse these networks, leading to
additional metadata and size considerations as data moves across multiple
devices and systems. Moreover, IoT devices may have limited storage and
bandwidth capacities. Transmitting, storing, and managing large provenance
datasets can load these resources, impacting the overall performance and
efficiency of IoT networks. There exist some solutions to overcome this
problem such as compression techniques. These techniques have high loss rate
and increase the computational complexity of the system with limited
resources. It is challenging to take into account the required information to
be stored in provenance records and maintain, at the same time, processing and
storage overheads. Addressing the challenge of provenance size in IoT networks
requires careful consideration of storage solutions, data compression
techniques, and protocols for optimizing data transfer and processing.
Balancing the need for detailed provenance information with the constraints of
IoT environments is very important for effective and efficient provenance
management in IoT networks.

A second challenge deals with _provenance attachment to data packets_. Making
sure provenance flows with data is a challenging task. Provenance is a type of
metadata which increases in size as the number of forwarding nodes increases.
In an IoT network, data often traverses diverse and resource-constrained
devices, making it essential to track the origin, transformations, and actions
performed on the data. Embedding provenance information directly into data
packets can be challenging due to constraints such as limited bandwidth,
energy, and processing capabilities of IoT devices. Maintaining a balance
between the requirements of data provenance and the limitations of IoT
networks is essential. Addressing this issue involves developing efficient and
lightweight methods for attaching and transmitting provenance records with
data packets and ensuring that the provenance information is captured
throughout the data path across the IoT network without causing significant
overhead or affecting the functionality of the devices.

Two additional challenges are related to _provenance collection_ and
_provenance privacy_. The former, provenance collection, deals with the large
amounts of data generated, usually in real time, in IoT network. This requires
the need for tracking its origin, transformations, and actions resulting in an
overhead for collecting provenance records. Additionally, the heterogeneity of
IoT devices introduces complexities in standardizing provenance formats, as
different devices may generate diverse data types and use different
communication protocols. The resource-constrained nature of many IoT devices
makes provenance collection more difficult, as it requires energy consumption,
storage limitations, and processing capabilities. Furthermore, extracting the
provenance information from the networks that are designed without considering
the possibility of need for querying provenance information is a challenging
task.

Provenance privacy emerges as a challenge in IoT networks due to the sensitive
nature of data and the wide number of different interconnected devices.
Provenance records, which trace the origin and transformations of data,
provide details about the context and usage history of information. In an IoT
system, where diverse and personal data is continuously generated, maintaining
the privacy of individuals becomes essential. The challenge is to balance the
need for important provenance information while protecting user privacy.
Dealing with these issues requires careful consideration of secure
transmission, encryption protocols, secure storage and access controls to
ensure that while provenance records remain effective for achieving security
requirements, they do not compromise the privacy rights of individuals and
entities whose data contributes to the IoT network. Developing robust privacy-
preserving mechanisms becomes essential to address these concerns and achieve
trust in the secure development of IoT networks.

##  8\. Conclusion

This review explores the integration of IoT and data provenance, addressing
the vulnerabilities in IoT networks and the need to ensure data
trustworthiness, data quality, traceability, and security. The IoT, with its
huge network of interconnected physical objects, has engaged applications in
many domains, but its risk of cyber attacks is a growing concern. This paper
aims to fill the gap by analyzing the integration of data provenance with IoT
networks which is considered as a security measure to securely transmit IoT
data and ensure data trustworthiness, a concept originally used in
heterogeneous database systems. The core objective of our research was to
provide a detailed review of existing data provenance techniques in IoT
networks, presenting their advantages, limitations, and their application in
ensuring security requirements. We also examined the encountered attacks and
performance metrics used to evaluate security requirements and system
efficiency. Through a well structured taxonomy, we categorized different
attributes related to the development of data provenance in IoT, helping
researchers for a better understanding of this field.

The findings of this study provide the importance of addressing the security
challenges in an environment characterized by rapid data transmission, limited
storage capabilities, bandwidth constraints, and energy limitations in IoT
networks. We have highlighted the need for secure, efficient and practical
implementation of data provenance techniques. Moreover, we have identified
that, while data provenance has been extensively studied in many domains, it
remains an under explored area in the context of IoT networks. The lack of a
systematic literature review in this field motivated us to conduct this
research, serving as a valuable resource for researchers. We have also
addressed the following research questions: (1) How data provenance is linked
to IoT networks?; (2) What are the provenance storage techniques, attacks and
security requirements when integrating provenance with IoT?; (3) How can data
provenance security approaches for IoT networks be categorized?; (4) What are
the existing practical implementations of data provenance in IoT?; (5) What
are the advantages and limitations of the proposed techniques for data
provenance in IoT in the studied literature? and (6) What are the main
research gaps and challenges in the domain of data provenance in IoT
networks?. In our discussion, we have explored the differences between our
review and the related research, presenting unresolved issues, research
challenges and potential directions for future research. Hence, we have
addressed both the significant challenges and the evolving issues within data
provenance in IoT.

## References

  * (1)
  * Akram and Ko (2014) Raja Naeem Akram and Ryan K. L. Ko. 2014.  Unified Model for Data Security - A Position Paper. In _Proceedings of the 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications_ _(TRUSTCOM ’14)_. IEEE Computer Society, USA, 831–839.  https://doi.org/10.1109/TrustCom.2014.110
  * Akyildiz et al. (2002) I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. 2002\.  Wireless sensor networks: a survey.  _Computer Networks_ 38, 4 (2002), 393–422.  https://doi.org/10.1016/S1389-1286(01)00302-4
  * Al-Rakhami and Al-Mashari (2022) Mabrook S. Al-Rakhami and Majed Al-Mashari. 2022.  ProChain: Provenance-Aware Traceability Framework for IoT-Based Supply Chain Systems.  _IEEE Access_ 10 (2022), 3631–3642.  https://doi.org/10.1109/ACCESS.2021.3135371
  * Alam and Wang (2021) Md Morshed Alam and Weichao Wang. 2021.  A Comprehensive Survey on Data Provenance: State-of-the-Art Approaches and Their Deployments for IoT Security Enforcement.  _J. Comput. Secur._ 29, 4 (jan 2021), 423–446.  https://doi.org/10.3233/JCS-200108
  * Alam and Fahmy (2014) S.M. Iftekharul Alam and Sonia Fahmy. 2014.  A practical approach for provenance transmission in wireless sensor networks.  _Ad Hoc Networks_ 16 (2014), 28–45.  https://doi.org/10.1016/j.adhoc.2013.12.001
  * Alexander et al. (2012) Roger Alexander, Anders Brandt, JP Vasseur, Jonathan Hui, Kris Pister, Pascal Thubert, P Levis, Rene Struik, Richard Kelsey, and Tim Winter. 2012\.  RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks.  RFC 6550.  https://doi.org/10.17487/RFC6550
  * Ali et al. (2014) Syed Taha Ali, Vijay Sivaraman, Diethelm Ostry, Gene Tsudik, and Sanjay Jha. 2014.  Securing First-Hop Data Provenance for Bodyworn Devices Using Wireless Link Fingerprints.  _IEEE Transactions on Information Forensics and Security_ 9, 12 (2014), 2193–2204.  https://doi.org/10.1109/TIFS.2014.2357998
  * Alkhalil et al. (2019) Adel Alkhalil, Rabie Ramadan, and Aakash Ahmad. 2019\.  Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks.  _International Journal of Advanced Computer Science and Applications_ 10, 6 (2019).  https://doi.org/10.14569/IJACSA.2019.0100650
  * Alkhalil and Ramadan (2017) Adel Alkhalil and Rabie A. Ramadan. 2017.  IoT Data Provenance Implementation Challenges.  _Procedia Computer Science_ 109 (2017), 1134–1139.  https://doi.org/10.1016/j.procs.2017.05.436 8th International Conference on Ambient Systems, Networks and Technologies, ANT-2017 and the 7th International Conference on Sustainable Energy Information Technology, SEIT 2017, 16-19 May 2017, Madeira, Portugal.
  * Aman et al. (2019) Muhammad Naveed Aman, Mohammed Haroon Basheer, and Biplab Sikdar. 2019.  Data Provenance for IoT With Light Weight Authentication and Privacy Preservation.  _IEEE Internet of Things Journal_ 6, 6 (2019), 10441–10457.  https://doi.org/10.1109/JIOT.2019.2939286
  * Aman et al. (2021) Muhammad Naveed Aman, Mohamed Haroon Basheer, and Biplab Sikdar. 2021.  A Lightweight Protocol for Secure Data Provenance in the Internet of Things Using Wireless Fingerprints.  _IEEE Systems Journal_ 15, 2 (2021), 2948–2958.  https://doi.org/10.1109/JSYST.2020.3000269
  * Ametepe et al. (2021) Wolali Ametepe, Changda Wang, Selasi Kwame Ocansey, Xiaowei Li, and Fida Hussain. 2021.  Data provenance collection and security in a distributed environment: a survey.  _International Journal of Computers and Applications_ 43, 1 (2021), 11–25.  https://doi.org/10.1080/1206212X.2018.1501937
  * Ammar et al. (2018) Mahmoud Ammar, Giovanni Russello, and Bruno Crispo. 2018\.  Internet of Things: A survey on the security of IoT frameworks.  _Journal of Information Security and Applications_ 38 (2018), 8–27.  https://doi.org/10.1016/j.jisa.2017.11.002
  * Araghi et al. (2023) Tanya Koohpayeh Araghi, David Megías, and Andrea Rosales. 2023\.  Evaluation and Analysis of Reversible Watermarking Techniques in WSN for Secure, Lightweight Design of IoT Applications: A Survey. In _Advances in Information and Communication_ , Kohei Arai (Ed.). Springer Nature Switzerland, Cham, 695–708.
  * Asghar et al. (2012) Muhammad Rizwan Asghar, Mihaela Ion, Giovanni Russello, and Bruno Crispo. 2012.  Securing Data Provenance in the Cloud. In _Open Problems in Network Security_ , Jan Camenisch and Dogan Kesdogan (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 145–160.  https://doi.org/10.1007/978-3-642-27585-2_12
  * Ateniese et al. (2017) Giuseppe Ateniese, Bernardo Magri, Daniele Venturi, and Ewerton R. Andrade. 2017.  Redactable Blockchain - Or - Rewriting History in Bitcoin and Friends. In _Proceedings - 2nd IEEE European Symposium on Security and Privacy, EuroS and P 2017_. Institute of Electrical and Electronics Engineers Inc., 111 – 126.  https://doi.org/10.1109/EuroSP.2017.37
  * Atzori et al. (2010) Luigi Atzori, Antonio Iera, and Giacomo Morabito. 2010\.  The Internet of Things: A survey.  _Computer Networks_ 54, 15 (2010), 2787–2805.  https://doi.org/10.1016/j.comnet.2010.05.010
  * Ayoade et al. (2020) Gbadebo Ayoade, Khandakar Ashrafi Akbar, Pracheta Sahoo, Yang Gao, Anmol Agarwal, Kangkook Jee, Latifur Khan, and Anoop Singhal. 2020.  Evolving Advanced Persistent Threat Detection using Provenance Graph and Metric Learning. In _2020 IEEE Conference on Communications and Network Security (CNS)_. 1–9.  https://doi.org/10.1109/CNS48642.2020.9162264
  * Backes et al. (2016) Michael Backes, Niklas Grimm, and Aniket Kate. 2016\.  Data Lineage in Malicious Environments.  _IEEE Transactions on Dependable and Secure Computing_ 13, 2 (2016), 178–191.  https://doi.org/10.1109/TDSC.2015.2399296
  * Bany Taha et al. (2015) Mohammad M. Bany Taha, Sivadon Chaisiri, and Ryan K. L. Ko. 2015\.  Trusted Tamper-Evident Data Provenance. In _2015 IEEE Trustcom/BigDataSE/ISPA_ , Vol. 1. 646–653.  https://doi.org/10.1109/Trustcom.2015.430
  * Baracaldo et al. (2017) Nathalie Baracaldo, Luis Angel D. Bathen, Roqeeb O. Ozugha, Robert Engel, Samir Tata, and Heiko Ludwig. 2017\.  Securing Data Provenance in Internet of Things (IoT) Systems. In _Service-Oriented Computing – ICSOC 2016 Workshops_ , Khalil Drira, Hongbing Wang, Qi Yu, Yan Wang, Yuhong Yan, François Charoy, Jan Mendling, Mohamed Mohamed, Zhongjie Wang, and Sami Bhiri (Eds.). Springer International Publishing, 92–98.  https://doi.org/10.1007/978-3-319-68136-8_9
  * Barre et al. (2019) Mathieu Barre, Ashish Gehani, and Vinod Yegneswaran. 2019\.  Mining Data Provenance to Detect Advanced Persistent Threats. In _11th International Workshop on Theory and Practice of Provenance (TaPP 2019)_. USENIX Association, Philadelphia, PA, 1–11.  https://www.usenix.org/conference/tapp2019/presentation/barre
  * Berrada and Cheney (2019) Ghita Berrada and James Cheney. 2019.  Aggregating unsupervised provenance anomaly detectors. In _11th International Workshop on Theory and Practice of Provenance (TaPP 2019)_. USENIX Association, Philadelphia, PA, 1–9.  https://www.usenix.org/conference/tapp2019/presentation/berrada
  * Berrada et al. (2020) Ghita Berrada, James Cheney, Sidahmed Benabderrahmane, William Maxwell, Himan Mookherjee, Alec Theriault, and Ryan Wright. 2020\.  A baseline for unsupervised advanced persistent threat detection in system-level provenance.  _Future Generation Computer Systems_ 108 (2020), 401–413.  https://doi.org/10.1016/j.future.2020.02.015
  * Bertino (2018) Elisa Bertino. 2018\.  Security and Privacy in the IoT. In _International Conference on Information Security and Cryptology_. Springer, Cham, 3–10.  https://doi.org/10.1007/978-3-319-75160-3_1
  * Botta et al. (2016) Alessio Botta, Walter de Donato, Valerio Persico, and Antonio Pescapé. 2016.  Integration of Cloud computing and Internet of Things: A survey.  _Future Generation Computer Systems_ 56 (2016), 684–700.  https://doi.org/10.1016/j.future.2015.09.021
  * Brzuska et al. (2009) Christina Brzuska, Marc Fischlin, Tobias Freudenreich, Anja Lehmann, Marcus Page, Jakob Schelbert, Dominique Schröder, and Florian Volk. 2009.  Security of Sanitizable Signatures Revisited. In _Public Key Cryptography – PKC 2009_. Springer Berlin Heidelberg, Berlin, Heidelberg, 317–336.  https://doi.org/10.1007/978-3-642-00468-1_18
  * Buldas et al. (2013) Ahto Buldas, Andres Kroonmaa, and Risto Laanoja. 2013\.  Keyless Signatures’ Infrastructure: How to Build Global Distributed Hash-Trees. In _Secure IT Systems_ , Hanne Riis Nielson and Dieter Gollmann (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 313–320.
  * Buldas et al. (2014) Ahto Buldas, Ahto Truu, Risto Laanoja, and Rainer Gerhards. 2014\.  Efficient Record-Level Keyless Signatures for Audit Logs. In _Secure IT Systems_ , Karin Bernsmed and Simone Fischer-Hübner (Eds.). Springer International Publishing, Cham, 149–164.
  * Buneman et al. (2001a) Peter Buneman, Sanjeev Khanna, and Tan Wang-Chiew. 2001a.  Why and Where: A Characterization of Data Provenance. In _Database Theory — ICDT 2001_ , Jan Van den Bussche and Victor Vianu (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 316–330.  https://doi.org/10.1007/3-540-44503-X_20
  * Buneman et al. (2001b) Peter Buneman, Sanjeev Khanna, and Tan Wang-Chiew. 2001b.  Why and Where: A Characterization of Data Provenance. In _Database Theory — ICDT 2001_ , Jan Van den Bussche and Victor Vianu (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 316–330.
  * Buyrukbilen and Bakiras (2014) Sahin Buyrukbilen and Spiridon Bakiras. 2014.  Secure Similar Document Detection with Simhash. In _Secure Data Management_ , Willem Jonker and Milan Petković (Eds.). Springer International Publishing, Cham, 61–75.  https://doi.org/10.1007/978-3-319-06811-4_12
  * Cachin (2016) Christian Cachin. 2016\.  Architecture of the hyperledger blockchain fabric. In _Workshop on distributed cryptocurrencies and consensus ledgers_ , Vol. 310. 4–8.  https://www.zurich.ibm.com/dccl/papers/cachin_dccl.pdf
  * Camenisch et al. (2017) Jan Camenisch, David Derler, Stephan Krenn, Henrich C. Pöhls, Kai Samelin, and Daniel Slamanig. 2017.  Chameleon-Hashes with Ephemeral Trapdoors. In _Public-Key Cryptography – PKC 2017_ , Serge Fehr (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 152–182.  https://doi.org/10.1007/978-3-662-54388-7_6
  * Chang et al. (2022) Byeong-Mo Chang, Kyung-Min Lee, Ga-Young Koh, and Kwanghoon Choi. 2022.  SmartProvenance: User-friendly provenance system for internet of things applications based on event flow graphs.  _IET Software_ 16 (2022), 576–602.  https://doi.org/10.1049/sfw2.12071
  * Charikar (2002) Moses S. Charikar. 2002\.  Similarity Estimation Techniques from Rounding Algorithms. In _Proceedings of the Thiry-Fourth Annual ACM Symposium on Theory of Computing_ (Montreal, Quebec, Canada) _(STOC ’02)_. Association for Computing Machinery, New York, NY, USA, 380–388.  https://doi.org/10.1145/509907.509965
  * Chen et al. (2010) Lianipng Chen, Muhammad Ali Babar, and He Zhang. 2010\.  Towards an Evidence-Based Understanding of Electronic Data Sources. In _Proceedings of the 14th International Conference on Evaluation and Assessment in Software Engineering_ (UK) _(EASE’10)_. BCS Learning & Development Ltd., Swindon, GBR, 135–138.  https://doi.org/doi/10.5555/2227057.2227074
  * Cheney et al. (2009) James Cheney, Laura Chiticariu, and Wang-Chiew Tan. 2009\.  Provenance in Databases: Why, How, and Where.  _Foundations and Trends® in Databases_ 1, 4 (2009), 379–474.  https://doi.org/10.1561/1900000006
  * Chenli et al. (2022) Changhao Chenli, Wenyi Tang, Frank Gomulka, and Taeho Jung. 2022\.  ProvNet: Networked bi-directional blockchain for data sharing with verifiable provenance.  _J. Parallel and Distrib. Comput._ 166 (2022), 32–44.  https://doi.org/10.1016/j.jpdc.2022.04.003
  * Chia et al. (2017) Ming Hong Chia, Sye Loong Keoh, and Zhaohui Tang. 2017\.  Secure Data Provenance in Home Energy Monitoring Networks. In _Proceedings of the 3rd Annual Industrial Control System Security Workshop_ (San Juan, PR, USA) _(ICSS 2017)_. Association for Computing Machinery, New York, NY, USA, 7–14.  https://doi.org/10.1145/3174776.3174778
  * Co. (2023) Samsung Electronics Co. Accessed 1 August 2023.  SmartThings Groovy IDE.  https://graph.api.smartthings.com/
  * Cox et al. (2007) Ingemar Cox, Matthew Miller, Jeffrey Bloom, Jessica Fridrich, and Ton Kalker. 2007.  _Digital Watermarking and Steganography_.  Morgan Kaufmann Publishers Inc.  https://doi.org/10.1016/B978-0-12-372585-1.X5001-3
  * Cui et al. (2000) Yingwei Cui, Jennifer Widom, and Janet L. Wiener. 2000\.  Tracing the Lineage of View Data in a Warehousing Environment.  _ACM Trans. Database Syst._ 25, 2 (jun 2000), 179–227.  https://doi.org/10.1145/357775.357777
  * Dai et al. (2008) Chenyun Dai, Dan Lin, Elisa Bertino, and Murat Kantarcioglu. 2008\.  An Approach to Evaluate Data Trustworthiness Based on Data Provenance. In _Secure Data Management_ , Willem Jonker and Milan Petković (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 82–98.  https://doi.org/10.1007/978-3-540-85259-9_6
  * Du et al. (2017) Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar. 2017\.  DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning. In _Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security_ (Dallas, Texas, USA) _(CCS ’17)_. Association for Computing Machinery, New York, NY, USA, 1285–1298.  https://doi.org/10.1145/3133956.3134015
  * Dunkels et al. (2004) A. Dunkels, B. Gronvall, and T. Voigt. 2004.  Contiki - a lightweight and flexible operating system for tiny networked sensors. In _29th Annual IEEE International Conference on Local Computer Networks_. 455–462.  https://doi.org/10.1109/LCN.2004.38
  * Elkhodr and Alsinglawi (2020) Mahmoud Elkhodr and Belal Alsinglawi. 2020.  Data provenance and trust establishment in the Internet of Things.  _Security and Privacy_ 3 (11 2020), 1–11.  https://doi.org/10.1002/spy2.99
  * Elkhodr et al. (2016) Mahmoud Elkhodr, Seyed Shahrestani, and Hon Cheung. 2016\.  A Middleware for the Internet of Things.  _International Journal of Computer Networks and Communications_ 8 (04 2016), 159–178.  https://doi.org/10.5121/ijcnc.2016.8214
  * Fallahpour and Megías (2015) Mehdi Fallahpour and David Megías. 2015.  Audio Watermarking Based on Fibonacci Numbers.  _IEEE/ACM Transactions on Audio, Speech, and Language Processing_ 23, 8 (2015), 1273–1282.  https://doi.org/10.1109/TASLP.2015.2430818
  * Foster (2003) I. Foster. 2003\.  The virtual data grid: a new model and architecture for data-intensive collaboration. In _15th International Conference on Scientific and Statistical Database Management, 2003._ 11–.  https://doi.org/10.1109/SSDM.2003.1214945
  * Gao et al. (2010) Ming Gao, Cheqing Jin, Xiaoling Wang, Xiuxia Tian, and Aoying Zhou. 2010.  A Survey on Management of Data Provenance: A Survey on Management of Data Provenance.  _Chinese Journal of Computers_ 33 (2010), 373–389.
  * Gao et al. (2018a) Peng Gao, Xusheng Xiao, Ding Li, Zhichun Li, Kangkook Jee, Zhenyu Wu, Chung Hwan Kim, Sanjeev R. Kulkarni, and Prateek Mittal. 2018a.  SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection. In _27th USENIX Security Symposium (USENIX Security 18)_. USENIX Association, Baltimore, MD, 639–656.  https://www.usenix.org/conference/usenixsecurity18/presentation/gao-peng
  * Gao et al. (2018b) Peng Gao, Xusheng Xiao, Zhichun Li, Fengyuan Xu, Sanjeev R. Kulkarni, and Prateek Mittal. 2018b.  AIQL: Enabling Efficient Attack Investigation from System Monitoring Data. In _2018 USENIX Annual Technical Conference (USENIX ATC 18)_. USENIX Association, Boston, MA, 113–126.  https://www.usenix.org/conference/atc18/presentation/gao
  * Gao et al. (2013) Yi Gao, Wei Dong, Chun Chen, Jiajun Bu, Gaoyang Guan, Xuefeng Zhang, and Xue Liu. 2013.  Pathfinder: Robust path reconstruction in large scale sensor networks with lossy links. In _2013 21st IEEE International Conference on Network Protocols (ICNP)_. 1–10.  https://doi.org/10.1109/ICNP.2013.6733600
  * Garchery and Granitzer (2020) Mathieu Garchery and Michael Granitzer. 2020.  ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level.  _CoRR_ abs/2007.06985 (2020).  arXiv:2007.06985 https://arxiv.org/abs/2007.06985
  * Glavic and Dittrich (2007) Boris Glavic and Klaus R. Dittrich. 2007.  Data Provenance: A Categorization of Existing Approaches. In _Datenbanksysteme für Business, Technologie und Web_. 227–241.  https://www.zora.uzh.ch/id/eprint/24450/
  * Gnawali et al. (2009) Omprakash Gnawali, Rodrigo Fonseca, Kyle Jamieson, David Moss, and Philip Levis. 2009.  Collection tree protocol. In _Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 2009_. 1–14.  https://doi.org/10.1145/1644038.1644040
  * Gosavi and Mali (2015) Chhaya S Gosavi and Suresh N Mali. 2015.  Video authentication and copyright protection using unique watermark generation technique and singular value decomposition.  _International Journal of Computer Applications_ 123, 3 (2015).  https://doi.org/10.5120/ijca2015905255