# Comprehensive Analysis of Access Control Models in Edge Computing:
Challenges, Solutions, and Future Directions

Tao Xue, Ying Zhang, Yanbin Wang, Wenbo Wang, Shuailou Li, Haibin Zhang T. Xue
is with the Hangzhou Institute of Technology, Xidian University, Zhejiang
311231, China (e-mail: xuetao@xidian.edu.cn). Y. Zhang is with the Hangzhou
Institute of Technology, Xidian University, Zhejiang 311231, China (e-mail:
22151214356@stu.xidian.edu.cn).Y. Wang is with the Hangzhou Institute of
Technology, Xidian University, Zhejiang 311231, China (e-mail:
wybpaper@gmail.com).W.Wang is with the Institute of information engineering,
Chinese Academy of Science, and School of Cyber Security, University of
Chinese Academy of Sciences, Beijing 100085, China (e-mail:
wangwenbo@iie.ac.cn).S. Li is with the Institute of Information Engineering,
Chinese Academy of Sciences, and School of Cyber Security, University of
Chinese Academy of Sciences, Beijing 100085, China (e-mail:
lishuailou@iie.ac.cn).H. Zhang is with the School of Cyber Security, Xidian
University, Xi’an 710071, China (e-mail: hbzhang@mail.xidian.edu.cn). Add:
No.8 East Qiannong Road, Xiaoshan District, Hangzhou City, Zhejiang Province,
Zip code: 311231.

###### Abstract

Many contemporary applications, including smart homes and autonomous vehicles,
rely on the Internet of Things technology. While cloud computing provides a
multitude of valuable services for these applications, it generally imposes
constraints on latency-sensitive applications due to propagation delays. Edge
computing emerges as a complementary approach, positioning computing resources
closer to the data sources to reduce the latency, relieve the bandwidth
pressure on the cloud, and enhance data security. However, this introduces
notable challenges in access control—a critical aspect of safeguarding data.
We survey existing access control models used in edge computing. We begin by
introducing various typical models and providing our insights. We then
classify these models according to their application in different data
lifecycles—data collection, storage, and usage—and assess the role of
blockchain technology in enhancing security measures. Through a detailed
investigation, we aim to systematically clarify the solutions to these
challenges and discuss their implications. Finally, we envisage some potential
research directions to further develop access control technologies in edge
computing.

###### Index Terms:

Access Control, Edge Computing, Cloud Computing, Internet of Things, Data
Security.

††publicationid: pubid:

##  I Introduction

In the Internet of Things (IoT) era, massive devices generate enormous data,
which is collected, stored, and analyzed for fueling many contemporary
applications, such as smart homes and smart cities [1]. As the pivotal
infrastructure, cloud computing is used to harness these data. However, the
latency, mainly induced by the geographical distance between data sources and
cloud centers, would be unacceptable for some applications, such as virtual
realization and connected autonomous vehicles. To address these challenges,
edge computing acts as a synergistic technique to cloud computing, so that
computing resources are situated closer to data sources and large raw IoT data
is processed locally in edge nodes. Subsequently, the processed data could be
transmitted to the cloud for advanced analysis [2]. Therefore, edge computing
can help alleviate network burdens for the cloud, support latency-sensitive
applications, and simultaneously enhance data security [3].

Access control for data security is always accompanied by the development of
new technology [4, 3], and edge computing is no exception. Access control
ensures that only authorized users and devices can access sensitive data
stored at the edge nodes. The number of security incidents caused by internal
users or system errors at the edge nodes can be reduced through stringent
access control. Access control helps enterprises ensure that their edge
computing practices comply with regulations such as GDPR [5].

Access control is commonly developed based on many typical and popular access
control models, such as Capability-Based Access Control (CapBAC) [6], Role-
Based Access Control (RBAC) [7], Attribute-Based Access Control (ABAC) [8],
Data Usage Control (DUC) [9, 10], Group-Based Access Control (GBAC) [11],
Context-Aware Access Control (CAAC) [12], Risk-Aware Access Control (RAAC)
[13], Relationship-Based Access Control (ReBAC) [14], and Trust-Based Access
Control (TBAC) [15, 16]. In this survey, CAAC includes basic CAAC and derived
CAAC. Basic CAAC considers raw context data, e.g., location. Derived CAAC
considers trust, risk, and relationship (corresponding to TBAC, RAAC, and
ReBAC respectively), which should be computed based on raw data. Besides,
there are also typical cryptography-based access controls, such as Attribute-
Based Encryption (ABE). We will detail these access control models in Section
II.

To enhance data protection in edge computing, numerous studies have been
dedicated to exploring these access control models to realize their potential
improvements/implementations in edge computing scenarios. These studies
confront several significant challenges of access control, which edge
computing introduces. Firstly, a wide range of IoT devices in edge computing
often operate with limited resources like storage, computational power, and
communication capabilities. The resource-constrained environments pose
challenges for effectively implementing the access control mechanisms that
typically require many resources. Secondly, the latency in edge computing
results from various factors like task offloading, communication duration,
computational expenses, and security measure-induced overheads. To preserve
its low latency advantage, the implementation of access control systems must
not significantly extend these delays. Thirdly, the mobile nature of data
producers and consumers in edge computing scenarios necessitates flexibility
in access control mechanisms to accommodate this dynamism. Lastly, the
widespread geographical distribution of edge nodes challenges the scalability
of centralized access control systems and complicates trust management in such
a decentralized environment.

It is important to summarize these studies for the development of access
control technologies in edge computing. Several surveys on this topic have
been published, usually from a different perspective. Kayes et al. [17] make
the review for CAAC. They systematically review the basic contextual
conditions, e.g., temporal, location, and spatial, and the derived ones, e.g.,
relationship. And, they introduce how CAAC works with RBAC and ABAC for
adapting to the dynamic environment in edge computing. Zhang et al. [18]
present CapBAC, ABAC, ABE, RBAC, and DUC in edge computing. However, they do
not cover GBAC and CAAC. Alnefaie et al. [19] survey the CapBAC, ABAC, DUC,
and RBAC, meanwhile, introducing CAAC (basic and TBAC), but without ABE, GBAC,
RAAC, and ReBAC. Rasori et al. [20] focus on reviewing suitable ABE for IoT,
which assumes that the edge servers are untrusted. To spur the development of
DUC, Akaichi et al. [21] review various DUC proposals for their specification,
enforcement, and robustness.

While the aforementioned studies make their reviews with different access
control models, they do not consider blockchain-based access control, which is
an emerging and widely adopted novel access control technology. Pal et al.
[22] mainly present the studies to use blockchain for enhancing CapBAC, RBAC,
TBAC, and ABAC in edge computing. Riabi et al. [23] present blockchain-based
access control for improving ABAC, RBAC, and CapBAC. Khan et al. [24] classify
the existing authorization mechanisms, weaknesses, and loopholes. The CapBAC,
ABAC, ABE, TBAC, and their corresponding blockchain-based schemes are
involved. Dramé-Maigné et al. [25] survey the access control solutions from
the perspectives of architectures, e.g., centralized, hierarchical, federated,
and distributed. The CapABC, ABAC, ABE, RBAC, CAAC, and general blockchain-
based access control are analyzed in these architectures.

Moreover, Ravidas et al. [26] divide the requirements of access control
systems into three categories according to policy lifecycles, i.e., policy
specification, policy management, and policy evaluation&enforcement. This
survey analyzes CapBAC, ABAC, DUC, RBAC, and CAAC according to the above
classifications.

However, the aforementioned studies do not consider the reviews from the
perspective of multiple data lifecycles. In edge computing, data security
encompasses multiple stages including data collection, storage, usage, etc. A
comprehensive review of access control techniques across multiple data
lifecycles can facilitate the holistic technique advancement. Khalid et al.
[27] survey on secure data storage and retrieval in edge computing. It
involves ABE and TBAC and discusses the corresponding management issues. Liu
et al. [28] conduct a comprehensive review on secure outsourced data analytics
in edge computing. Secure data collection covers authentication mechanisms and
trust management mechanisms. Secure data processing relies on the following
techniques: homomorphic encryption, differential privacy, pseudonym
technology, etc. Data processing generates computational results, which could
be stored and kept protected. Secure data storage usually leverages access
control mechanisms (e.g., ABE and RBAC) and searchable encryption (including
symmetric and asymmetric methods). However, this work does not involve CapBAC,
DUC, GBAC, and the related blockchain-based schemes.

Although there are surveys on edge computing access control according to
different data lifecycles, they do not systematically consider those typical
access control models in the third paragraph and lack the corresponding
blockchain schemes for each model. This motivates us to have a more
comprehensive and systematic survey with the following contributions:

  * •

According to the architecture of edge computing, we analyze the access control
requirements from data collection, data storage, and data usage, and the role
of blockchain in protecting critical access control data in these data
lifecycles. In particular, we show how to use/enhance the CapBAC, ABAC, ABE,
DUC, RBAC, GBAC, CAAC (basic, TBAC, RAAC, and ReBAC), and blockchain from the
perspective of these data lifecycles.

  * •

Following the systematic requirements analysis, we present and discuss the
recent studies on access control techniques in edge computing, and provide the
lessons learned from the involved studies.

  * •

According to the discussions about the recent research studies, we discuss
challenges and research directions of access control in edge computing.

For the reader’s convenience, we classify the related studies to be discussed
in this survey in Figure 1. Our classification does not cover every aspect of
access control due to the complexity of access control in edge computing.
However, this survey can complement existing surveys, which can help readers
understand edge computing access control systematically. Further, the
researcher or developer in the field can make systematical selections and
innovations based on the related access control techniques. This survey can
prompt the development of access control techniques in edge computing under
the intricacy of innovations in access control.

Figure 1: Classification of related studies to be discussed in this survey.

The rest of the paper is organized as follows. Section II introduces typical
access control models and blockchain technology with meaningful insights.
Section III describes background knowledge about edge computing, and then
presents our requirements analysis on access control in edge computing.
Sections IV&V&VI respectively introduce the recent access control solutions
according to the requirements. Further, Section VIII envisages some potential
research directions to foster research efforts. Section IX concludes this
paper.

##  II Typical access control models and Blockchain Technology

###  II-A Typical access control models

The access control model formally defines how subjects, objects, actions,
conditions, and authorization policies are represented. And, the authorization
policies regulate which object(s) can be accessed by which subject(s) with
which action(s) under what condition(s). An access control mechanism is
enforced according to an access control model and ensures that the objects can
be controlled and used legally by reasonable access control.

Many typical and popular access control models are available in academia and
industry, such as, Capability-Based Access Control (CapBAC) [6], Role-Based
Access Control (RBAC) [7], Attribute-Based Access Control (ABAC) [8], Data
Usage Control (DUC) [9, 10], Group-Based Access Control (GBAC) [11], Context-
Aware Access Control (CAAC) [12], Risk-Aware Access Control (RAAC) [13],
Relationship-Based Access Control (ReBAC) [14], and Trust-Based Access Control
(TBAC) [15, 16]. To organize this survey, we classify RAAC, ReBAC, and TBAC
into CAAC, and we will explain in the following.

Access Control Lists (ACLs) [29] are the most common access control, where the
access right on resources is represented by a sequence of pairs
$\langle$subject, operations$\rangle$. However, the list would become complex
with the increment of subjects and resources. To overcome the deficiency, RBAC
[7] introduces the role concept, which decouples the subjects and resources by
assigning rights to the role(s) of subjects. Thus, RBAC reduces the efforts to
manage access rights. However, in a large number of resources or cross-
organization scenarios, the role explosion still needs lots of effort to be
conquered. ABAC [8] uses properties of subjects, objects (resources), and
environment to specify access rights, which exploits more components to manage
access rights. This fashion could mitigate the role explosion problem but with
the expense of more complex attributes components and access rules. However,
the common problem of ACLs, RBAC, and ABAC is that they enforce access control
by checking the union of all rights in a request. To avoid unnecessary
traversal in the checking, CapBAC [6] authorizes access rights by subject-
oriented linked list, a sequence of pairs $\langle$object, rights$\rangle$.
CapBAC could achieve minimal privilege enforcement by checking the request-
specified legal capability. Efficient capability revocations and updating can
be easily enforced. However, CapBAC needs to issue capabilities for all
subjects by cryptographic techniques.

GBAC is a group-based model, where entities with the same permission
requirements are assigned to the same group. Each group has specific access
rights and attributes. Administrators can create groups of different levels as
needed to achieve finer access control. The inappropriate management of groups
likely induces poor access control performance.

We can further incorporate context information in access control to enhance
access permission management. In CAAC, access permissions are determined based
on the combination of the subject, object, and other contextual information.
CAAC can utilize this contextual information to adjust access permissions
dynamically, thereby better meeting specific requirements and enhancing
security. Contextual information may include basic ones, such as time,
location, device status, network conditions, etc, and derived ones, such as
trust, risk, relationship, etc. We mainly introduce the TBAC, RAAC, and ReBAC
for this survey. 1) TBAC: In the era of pervasive computing resources and IoT
devices, it is common that trust between entities cannot be established
beforehand. TBAC focuses on enforcing access control by evaluating the trust
level of the subject or object when trusted identities are not determined
mutually. 2) RAAC: In RAAC, the authorization of an access request depends on
the risk factors associated with that request. These risk factors may include
user behavior patterns, device security status, network security status, etc.
The system dynamically adjusts access permissions based on these risk factors
to provide appropriate resource access at different risk levels. 3) ReBAC:
ReBAC emphasizes the relationship between subjects in authorization decisions.
The relationship can be diversified, such as superior-subordinate,
cooperative, friendly, etc. Administrators can use the relationship to define
access rights.

The above access control models are non-cryptographic access control, which
only controls data access according to access control policies. However, the
data can be easily leaked if the access control is bypassed. With cryptography
for access control, we can control data access according to access control
policies while the protected data is encrypted [30]. The Attribute-Based
Encryption (ABE) is a popular scheme in this kind of approach. In an ABE-based
system, data decryption is contingent upon the alignment of attributes.
Specifically, both ciphertexts and users’ keys carry specific attributes. A
user can only decrypt the ciphertext if the user’s attributes (or access
policy) match the ciphertext’s access policy (or attributes). Key-Policy
Attribute-Based Encryption (KP-ABE) combines access policy with key and
encrypts data with attributes. Ciphertext-Policy Attributes-Based Encryption
(CP-ABE) combines attributes with keys and encrypts data with access policy.
ABE-based access control has mobility, i.e., each distributed data-hosting
party can be a policy enforcement point because the ciphertext of data is
combined with the access control. However, as asymmetric encryption
technology, ABE requires the extra management of the ciphertext and keys and
is generally used for small data volumes.

As shown in Figure 2, traditional ABE requires the authority to manage keys
based on legal attributes. Data owners and data users only interact with the
key intermediary. The abnormal key intermediary (e.g., disrupted legal
attributes, DOS attack because of hacker intrusion) can affect the whole ABE
system. Otherwise, multi-authority ABE (MA-ABE) depends on many related
attribute authorities, where we can configure attributes used for encryption
and decryption. For example, we can set that decryption can succeed with at
least $d_{k}$ authorities providing secret keys for data users. As such, we
can use the MA-ABE to manage attributes distributedly and resolve the key
escrow problem.

Figure 2: Attribute-based encryption (ABE) and multi-authority attribute-based
encryption (MA-ABE) [30].

The above models have a common drawback: they lose control after the data is
granted access. DUC restricts data usage to enforce more flexible control
after the data has been granted. For example, a party can use DUC to restrict
data usage after it shares its data with another party. The restriction rules
are specified by data owners who want to control how their data can be used.
DUC can satisfy multiple data sovereignty requirements [31]. For example, the
data usage scope controls that data must be used by trusted nodes or services;
the data usage purpose controls that data must be used for specific usage
purposes; the living time controls that data must be deleted immediately after
data is used. However, DUC needs additional expenditure to accomplish these
functions, such as purpose management and recognition.

###  II-B Blockchain Technology

The blockchain is a kind of distributed ledger technology [32, 33, 34]. The
committed transactions are recorded in the block. The linked block has the
cryptographic hash summary of the content of the previous block, which enables
the blockchain to be tamper-evident and tamper-resistant. Storing data in a
distributed blockchain network reduces the risk of a single point of failure.
The coded contract in a smart contract on a blockchain can be deployed to and
executed by blockchain nodes. And, smart contracts can satisfy the common
contract items and the need for trusted intermediaries. The execution results
are ensured to be correct due to the consensus mechanism in blockchain so that
the verification of calculation results can be avoided.

Blockchain can be divided into permissionless and permissioned blockchains
[32]. Permissionless blockchain allows anyone to join, e.g., the popular
public blockchain systems, bitcoin [35] and Ethereum [36]. Permissioned
blockchain allows legal users of blockchain to participate, e.g., the
consortium blockchain, Hyperledger Fabric [37].

##  III Edge Computing and Requirements Analysis on Access Control

###  III-A Edge Computing

Traditionally, the central cloud is the main platform to provide computing
power for a wealth of data from distributed heterogeneous IoT devices [38].
The traditional cloud-centric architecture involves long propagation delays,
which is unacceptable for latency-sensitive applications [39]. We can find
some latency-sensitive examples in [40]. For example, in a smart health
system, a large amount of data generated by smart sensors for telemedicine
needs to be processed in microsecond latency. In the telemedicine scenario,
the high-solution videos need to be shown to doctors in time. A visual guiding
service using a wearable camera requires a preferred response time between 25
ms to 50 ms.

To conquer the latency challenges in traditional cloud computing, the end-
edge-cloud system architecture, as shown in Figure 3, is proposed with these
layered and connected components: cloud, edge nodes, and end devices [3],
where data can be processed closer to the data source. In particular, edge
nodes connect with pervasive heterogeneous IoT devices, and interact with the
cloud, meanwhile, an edge computing orchestrator could orchestrate cooperative
work among edge nodes/cloud [41]. Massive data collected from IoT devices can
be aggregated at edge nodes distributedly before the data is delivered to the
cloud. Real-time data processing and basic analytics can be fulfilled at the
edge. Due to the advantages of edge computing, it has been widely used in many
applications as shown in Figure 3, including smart transportation, smart sea
monitoring, smart homes, smart health, etc.

Figure 3: The end-edge-cloud system architecture [3].

###  III-B Requirements Analysis on Access Control

This section would describe the access control requirements for edge computing
in the tri-layer end-edge-cloud architecture. Traditional cloud computing
requires large raw data to be centrally uploaded to the cloud servers for
unified processing, which increases the risk of data loss and leakage [42].
However, edge computing enables data to be processed locally by deployed edge
servers, reducing the need to transfer data to the cloud servers for
processing, thus enhancing data privacy and security. With massive data
processed at the edge, access control, as the common fundamental technique for
protecting data, should be studied thoroughly. To have a clear analysis of
access control requirements, we first show the following natural
characteristics of edge computing, which would challenge the access control at
the edge [43, 44]:

  * •

Limited resources: As shown in Figure 3, various smart devices are at the end.
However, these devices often have constraints on resources (e.g., energy,
storage, computation, and communication), while executing access control
generally requires enough resources.

  * •

Low latency: Edge computing embodies latency-sensitive applications. In
particular, the latency in edge computing includes many aspects: task-
offloading time, communication latency, computation cost, security-induced
cost, etc. [45, 46]. The total of these delays should be as small as possible.
The low latency of edge computing should not be negated by those aspects.

  * •

Flexibility: Data producers and consumers are generally dynamic. In
particular, unbound IoT devices may be added; short-lived interaction is
typical; services are potentially changing [44]. As such, the dynamic nature
of data producers and consumers in edge-computing scenarios necessitates
flexibility in security measures to accommodate this dynamism.

  * •

Scalability: The decentralized architecture of edge computing allocates
computing resources at scale [44]. In this way, the widespread geographical
distribution of edge nodes would naturally challenge the scalability of
centralized security mechanisms and complicate trust management in such a
decentralized environment.

Figure 4: Access control requirements analysis in edge computing.

Corresponding to the above characteristics of edge computing, the access
control of edge computing should depend on small resources and have low delay,
flexibility, and scalability. The typical models and blockchain technology
introduced in Section II can be used to satisfy the requirements.
Specifically, we would analyze how to satisfy these requirements in this
survey from the perspectives of the following common data lifecycles: data
collection, data storage, and data usage [47].

To clarify the requirements analysis, we first generalize the basic process of
service provisioning and utilization in the tri-layer end-edge-cloud
architecture according to the studies [48, 49, 50]. Figure 4 (I) shows four
kinds of stakeholders in edge computing services: users, service providers,
edge computing providers, and cloud computing providers [51]. Generally,
service providers register on the cloud (ⓐ), and cloud computing providers
authorize corresponding services on legal edge servers (ⓑ). Then, users
register services from service providers(ⓒ), and users can get registered
services from edge servers of edge computing providers (ⓓ). In this paper, IoT
devices and edge services act as various users. To describe the access control
requirements, we generalize three kinds of services here, i.e., data
collection service, data storage service, and data usage service (as shown in
Figure 4 (II)), which correspond to each data lifecycle respectively.

Data Collection: As shown in Figure 4 (II), IoT devices generate large amounts
of data. Because IoT devices commonly have limited computation and storage
resources or some services require various data from IoT devices, IoT-
generated data is uploaded to edge servers for the related services. In
particular, as shown in the green box in Figure 4 (II), ①③arrows indicate the
end$\rightarrow$edge data collection, i.e., collecting data to use it at edge;
② arrow indicates another end$\rightarrow$edge data collection, i.e.,
collecting data to store it at edge. In the process, access control should
determine which IoT-generated raw data can be accessed by which services,
meanwhile the access control should be flexible given the flexibility of end
devices and services (denoted as RC1 requirement). The following access
control models can be used for satisfying RC1: GBAC, ABAC, CapBAC, and CAAC.
Therein, GBAC can facilitate data management by leveraging the group concept
on IoT devices. ABAC could be suitable because it can adapt the across-domain
data collection. CapBAC consumes small resources of IoT devices due to its
method of authorization of access rights. CAAC can use basic context and
derived context information to further accommodate the flexibility of edge
computing.

Due to the end-edge-cloud architecture, as shown in Figure 4 (II), data
collection from edge to cloud or from edge to edge is commonplace [52]. In
particular, as shown in the green box in Figure 4 (II), ④ arrow indicates the
edge$\rightarrow$edge data collection, i.e., some data usage services produce
intermediate aggregation results, and data storage services may collect them
to store them. ⑤⑥⑦ arrows indicate the edge$\rightarrow$cloud data collection,
i.e., edge computing could achieve real-time data processing while cloud
services collect/sample some data at the edge for further advanced analysis.
In this situation, access control should determine which intermediate data can
be accessed by which edge services or cloud services (denoted as RC2
requirement). The required access control techniques include CapBAC, ABAC, and
CAAC similar to in RC1. Besides, ABE can prevent the collected small raw data
from being leaked on malicious edge servers.

Data Storage: Second, we inspect the access control for securely storing the
IoT-generated data and its related computation results, so that the stored
data can be securely used for mining valuable insights or interacting with the
IoT devices. In particular, due to limited resources on IoT devices, IoT-
generated data would be stored on edge servers, as mentioned ② arrow; and the
computation results of IoT-generated data could be stored on edge servers, as
mentioned ④ arrow. The stored IoT-generated data should be accessed by
corresponding authorized parties, which have different access rights to data
(denoted as RS1 requirement). The stored computation results may cooperate
among edge servers, where different edge services should access different
computation results (denoted as RS2 requirement).

TABLE I: Involved typical access control models in each classification in this
survey

LC AC | CapBAC | RBAC | ABAC | ABE | DUC | GBAC | CAAC (basic, TBAC, RAAC, ReBAC) | AC Data Management
---|---|---|---|---|---|---|---|---
Blockchain for AC in DC Blockchain for AC in DS Blockchain for AC in DU
Blockchain as VACP for multiple data lifecycles
[.5]0 DC | ✓ | - | ✓ | ✓ | - | ✓ | ✓,✓,✓,-
[.5]0 DS | - | ✓ | ✓ | ✓ | - | - | ✓,✓,✓,-
[.5]0 DU | - | ✓ | - | - | ✓ | - | ✓,✓,✓,✓

  * 1

AC: Access Control, LC: Lifecycle, DC: Data Collection, DS: Data Storage, DU:
Data Usage, VACP: Versatile Access Control Platform

Data cryptography for access control is practical for satisfying RS1 & RS2.
Edge servers commonly belong to third parties. The encrypted data ensures that
malicious third-party edge servers cannot peep data easily, meanwhile, the
native access control functions by cryptography can control which subject can
access which part of encrypted data [53]. ABE-based access control is
promising, where various attributes, e.g., role, context, trust, risk, etc.
can be used for regulating access policies flexibly. Moreover, ABE-based
access control has mobility, i.e., each distributed data-hosting party can be
a policy enforcement point because the ciphertext of data is combined with the
access control. It is a data-centric access control without centrally
depending on a policy enforcement point. ABE could prompt data sharing with
high flexibility and scalability in edge computing.

Data Usage: As shown in Figure 4 (II), the edge computing ecosystem has
pervasive data analytics. In particular, data analytics exists in some edge
servers, whose responsibility is to analyze raw data. For instance, as
mentioned ①③ arrows, the collected raw data is to be used. Besides, data
analytics also exists in some edge servers, which use some computation results
shared from other servers for high-level analysis, as ⑧ arrow indicates. Data
owners could expect to learn how their outsourced data is used and control its
usage (denoted as RU1 & RU2 requirements). However, the above access control
models in data collection and storage would end their responsibility for
protecting data after data access is granted. DUC could satisfy data
sovereignty requirements because it enforces further flexible control by data
usage restrictions.

Securing Critical Access Control Data: In summary, because the typical access
control models mentioned in the above data lifecycles are not derived from
edge computing natively, we should attempt to enforce them effectively in
distributed edge computing to achieve low resource consumption and delay for
access control, while ensuring flexibility and scalability. However, an access
control system generally includes a wealth of sensitive access control data
[54]. For example, CapBAC includes authorization tokens, token registration,
delegation, and revocation functions. ABAC contains various attributes,
attribute-based policies, and programs for enforcing policy. Some important
access control data should be protected, e.g., sensitive tokens, private
attributes, critical policies, and enforcement programs.

Blockchain can be used to store important access control data because, as
introduced in Section II-B, it is a distributed database for storing data and
the on-chain data is immutable. Moreover, accountability is natively provided
because blockchain has immutability and traceability properties. Besides,
smart contracts can be coded distributedly to enforce access control and the
security of enforcement results is guaranteed by the consensus mechanism, and
it can be used as a trustable alternative for enforcement programs. As shown
in Figure 4 (III)(1)(2)(3), blockchain technology can enhance the access
control mechanisms in data collection, storage, and usage, respectively. More
precisely, the access control requirements for each data lifecycle can be
achieved while safeguarding critical access control data.

Furthermore, the blockchain platform can also manage various access control
enforcement. In particular, the blockchain platform can embody multiple access
control functions, e.g., CapBAC for data collection, ABE for data storage, and
DUC for data usage. That is, as shown in Figure 4 (III)(4), a platform could
satisfy all access control requirements (RC1&RC2, RS1&RS2, and RU1&RU2)
meanwhile access control data for multiple data lifecycles is uniformly
managed.

Summary: We have described the access control requirements by the common
multiple lifecycles (including data collection, storage, and usage) while
analyzing blockchain’s role in protecting critical access control data. As
shown in Table I, we summarize the adopted typical access control models for
satisfying the access control requirements in each data lifecycle and
generalize the usage of blockchain to manage access control data. The
contextual properties of CAAC can be integrated into ABAC, ABE, and DUC. In
particular, the environmental properties of ABAC can use the contextual
properties of CAAC to achieve access control; ABE can use them to
encrypt/decrypt sensitive data; DUC can use them to control how to use data.
Moreover, the role in RBAC can be regarded as an attribute of ABAC/ABE and a
component of DUC. The integration can facilitate the flexibility of access
control. Finally, because consensus algorithms in blockchain possibly induce
large latency, we should protect critical access control data with acceptable
latency. Note that, we do not present exhaustive access control models for
each data lifecycle in edge computing due to the complexity. The researcher or
developer in the field can make systematical selections and innovations based
on the related access control techniques. This survey can prompt the
development of access control techniques in edge computing under the intricacy
of innovations in access control.

In the following sections, we will describe the recent solutions to use these
typical access control models as base models for implementing access control
in edge computing: 1) access control in data collection (Section IV), 2)
access control in data storage (Section V), 3) access control in data usage
(Section VI), and 4) blockchain-based access control platform (Section VII).

##  IV Access control in data collection

An abundance of raw data measured from IoT devices should be securely
collected for subsequent processing and valuable insights. Access control
should determine which IoT-generated raw data can be accessed by which
services, meanwhile the access control should be flexible given the
flexibility of end devices and services. For the processed raw data (i.e.,
intermediate data), access control should determine which intermediate data
can be accessed by which edge service or cloud service. In this section, we
present the studies according to the typical model used as the base model to
meet the access requirements, with the summary and lessons learned from these
studies.

GBAC as the base model: To protect IoT-generated data from tampering and
unauthorized access, group key management (GKM) is proposed [55]. A device
group key is shared with its current subscribers, and the device can encrypt
its data by using a group key and only the legal subscribers can decrypt it.
This ensures that authorized group users can access the data collected from
IoT devices. However, the GKM only assigns a symmetric group key to each group
and does not support members’ independence in the same group. This means that
keys need to be reassigned every time membership changes or state changes in a
group. The flexibility in the IoT environment can induce frequent key
redistribution and thus introduce large management and communication overhead.
To conquer the challenges, Dammak et al [56] propose a new decentralized
lightweight group key management architecture for access control. It
introduces a layered architecture, where a central key distribution center
accounts for the management of the device group. Each device group has several
user groups, and each user group corresponds to a sub-key distribution center
(SKDC). By dispersing the key management tasks across multiple SKDCs, the
system can better handle the complexity and overhead caused by changes in
membership. When a user joins or leaves a group, only the SKDC where the user
is located is adjusted.

TBAC as the base model: Jiang et al. [57] present a trust-based energy-
efficient data collection for unmanned aerial vehicles (UAV). A UAV trajectory
optimization algorithm recognizes the most data collection anchors, which
guarantees the data collection trajectory is shortest and extends network
life, meanwhile, a trust reasoning and evolution mechanism is used to identify
the trusted sensor nodes and guarantee the quality of collected data. This
method increases the network life by 48.9%, and achieves 91% accuracy in node
trust degree identification while consuming only 8% of network life.

RAAC as the base model: Atlam et al.[58] propose a dynamic and adaptive RAAC
model. The risk assessment algorithm considers contextual information,
resource sensitivity, action severity, and previous risk history. This model
can adapt to the dynamics of IoT. When an access request is issued for data
collection, the model can use the real-time characteristics of user requests
to estimate the security risk and make an access decision. However, this
solution is not evaluated.

TABLE II: Access control schemes in data collection Ref | Base Model | Requirements |  Performance |  Key Improvements
---|---|---|---|---
[55, 56] | GBAC | RC1 |  High flexibility and scalability, Storage cost (Bytes), Computation cost (us), and Communication cost (us). |  Introducing a decentralized lightweight group key management scheme for flexible and scalable data collection.
[57] | TBAC | RC1 |  Increasing the network life by 48.9%, and achieving 91% accuracy in node trust degree identification while consuming only 8% of network life |  Significantly improving the identification accuracy of node trust degree and greatly prolonging the network life.
[58] | RAAC | RC1 |  - |  Attempting to provide the algorithm for calculating the risk of user requests to adapt the flexibility and scalability of IoT devices.
[59, 60, 61] | CapBAC |  RC1 & RC2 |  Average latency of user token verification is 4.8 ms. Supporting efficient and quick token revocation for IoT devices and edge servers. |  Providing a distributed access control framework for pervasive edge computing services.
[62] | CP-ABE | RC2 |  The time overhead involves the key generation, encryption, and decryption. It is proportional to the number of attributes. |  Providing a protocol for encrypted key exchange based on CP-ABE to establish secure communication between edge nodes and the cloud.
[63] | CP-ABE | RC2 |  The encryption time is increased slightly because of the bilateral fuzzy matching. However, the decryption and communication overheads are decreased significantly due to the outsourced matching workload to edges nodes. |  Achieving data sharing with bilateral fuzzy matching in a secure cloud-edge communication environment.

CapBAC as the base model: An advanced access control framework (APECS) for
pervasive edge computing is proposed [59], where the legitimated end
devices/edge servers can lightly obtain services permission of available edge
servers, i.e., the access control for collecting data from end to edge and
edge to edge is supported simultaneously. APECS framework depends on the
CapBAC, where the authorized token for users by service providers is a tuple,
including the unique identifier of the service provider, a list of service
identifiers, a certificate of the user, a list of authorization levels, and
the token’s expiry time. The authentication and authorization tasks are
directly delegated to semi-trusted edge servers, rather than centralized cloud
servers, such that network communication beyond the edge is avoided. Moreover,
APECS supports efficient and quick token revocation for users and edge
servers. However, the authentication token contains the user’s identity, so
privacy leakage could happen once the user’s identity is parsed by servers. An
anonymous and auditable distributed access control framework for edge
computing (AADEC) is proposed [60] for overcoming the deficiency. In AADEC,
the user identity information is hidden, which raises the bar for attackers
sniffing user privacy. Nevertheless, because of the data confidentiality, it
is likely to induce fake data spreading issues. AADEC allows a trusted third
party to achieve malicious data auditing or tracing, which further mitigates
the threat in edge computing. Moreover, Liu et al. [61] improve the efficiency
of access control in APECS by achieving batch service authorization.

CP-ABE as the base model: To guarantee the confidentiality and verifiability
of transmitted encrypted data between edge nodes and the cloud, ARWA et al.
[62] design an encrypted key exchange protocol based on CP-ABE and digital
signature. In particular, the edge node’s shared key can only be obtained by
the cloud or other edge node(s) with a set of legal attributes. The
experimental results show that, in terms of the CP-ABE mechanism, the key
generation time, the encryption time, and the decryption time are increased
linearly with the number of attributes. However, the attributes in this
protocol are defined centrally, limiting access control’s flexibility. To
implement more flexible access control, Zhang et al. [63] propose a scheme
FADS, where edge nodes and cloud can specify access policies respectively and
fuzzy matching is allowed. The transmitted data can be decrypted if and only
if the receivers’ attributes successfully match the access policies defined by
two sides (sender and receiver). This matching is fuzzy, that is, receiver
attributes can satisfy access policy without a rigid access structure. This
way can hide the accurate user attributes, and enhance privacy protection.

###  IV-A Summary and Lessons Learned

In this section, we have discussed the studies to satisfy RC1 & RC2. The
approaches are summarized in Table II, and the learned lessons are as follows:

  * •

We can observe that these studies leverage various components to achieve
access control, which facilitates data collection within the dynamic
environment of edge computing. Some focus on developing algorithms to evaluate
access control components, such as group, trust, and risk. Others, addressing
the challenges of resource-constrained IoT devices, incorporate different
elements to create valid tokens in CapBAC. The high-performance implementation
of CapBAC enables the effective use of edge services for processing IoT raw
data. However, privacy concerns, particularly regarding the sensitive elements
in tokens, must be also addressed [60]. Furthermore, when combined with data
cryptography, these access control schemes can ensure secure and flexible data
collection for smart IoT devices [56].

  * •

Although components such as group, trust, risk, raw context, capability, and
cryptography facilitate adaptive data collection in the dynamic environment of
edge computing, their integration has not been thoroughly explored. For
instance, some IoT devices may integrate data from various sources to deliver
specific services. A high-performance CapBAC scheme is desirable, which would
support flexible and efficient token management—enabling capability
registration, delegation, and revocation. It should also allow for federated
operations, enabling collaborative service access across different data
sources, and optimize service location using additional information, such as
network topology from software-defined networks [64], group dynamics, and
trust levels.

##  V Access control in data storage

As learned from typical access control models, ABE-based access control is
popular data cryptography solutions for secure data storage in edge computing.
The stored raw data should be protected by access control policies specified
by data owners or administrators. The stored computation results may be
cooperated among edge nodes/cloud, where different edge/cloud services should
access different computation results. This section introduces the
corresponding recent studies.

###  V-A ABE-based access control

We recommend the surveys [20, 65] for readers to learn more about ABE. We
present the most recent studies, which include general ABE-based solutions,
generalizing the common problems in edge computing, and domain-specific ones,
which design ABE for edge computing of some domains. We first introduce the
general ABE-based solutions.

The linear relationship between the ciphertext/secret key sizes and the number
of attributes impedes the development of ABE-based solutions. To address this
issue, Sarma et al. [66] propose a multi-authority CP-ABE scheme MACFI, which
reduces the data size and outsources expensive decryption computation to edge
nodes. MACFI achieves that the size of the secret key is kept irrespective of
the number of attributes and that the ciphertext size is agnostic for the
number of attributes and has a linear relationship with the number of
authorities. MACFI further increases the efficiency of CP-ABE-based access
control for edge computing in IoT. Moreover, each attribute authority in MACFI
verifies a user, and allocates attributes based on the user’s role (RBAC). Xu
et al. [67] propose a new matchmaking attribute-based encryption (MABE) for
enhancing the performance of KP-ABE. On the one hand, MABE uses collision-
resistant hash functions to generate the ciphertexts, which decreases the
encryption time for IoT devices. On the other hand, like outsourcing
ciphertext decryption to edge nodes, MABE also outsources heavy ciphertext
identification to edge nodes, but edge nodes can leverage the access
structures from data receivers to filter useless data in the ciphertext. As
such, the decryption time is decreased largely. As shown in the experiments,
this way not only decreases the computation overhead for IoT devices garnering
useful data but also saves the cost of bandwidth.

Changing the mathematics in cryptography is another way to cut down the
computation burden. To consume much fewer resources, Rui et al. [68] propose a
novel CP-ABE scheme based on elliptic curve cryptography. As a result, the
expensive bilinear pairing operation in native CP-ABE is replaced with simple
scalar multiplication. In addition, this study considers the time and location
attributes for encryption and also outsources expensive computational tasks to
edge servers. As shown by the experiments, the encryption of time and location
attributes consumes a few scalar multiplication operations, and the decryption
time has no obvious linear relationship with the number of attributes because
the resource-limited IoT devices only calculate the scalar multiplication
operation one time.

The aforementioned studies commonly improve ABE-based access control
efficiency by offloading the decryption computation burden to edge nodes. To
further improve the efficiency, Xu et al. [69] reduce the cost of data
decryption simultaneously by outsourcing the workloads from these aspects:
data source identification, revocable storage, and decryption mechanism.

IoT gateway can sense the context information with IoT devices or vicinity
users, e.g., location, time, and sensitivity. The context information often
changes continuously and frequently, making the static and traditional
security solutions inefficient. For example, Rui et al. [68] simply combine
contextual attributes (time and location) with their CP-ABE solution. Arfaoui
et al. [70] propose a context-aware attribute-based access control (CAABAC)
approach for providing data consumers with secure and adaptive remote control
of smart IoT. CAABAC incorporates the CP-ABE with contextual information to
achieve adaptive contextual data collection for data consumers, where
contextual tokens are integrated into the CP-ABE’s access structure. When data
consumers decrypt the collected data, they are required an attribute set and
context information to collectively satisfy the access structure. In this
solution, IoT gateways act as edge nodes, which store the encrypted data. The
experiments show that small computation cost is consumed on IoT devices.
However, the most existing CP-ABE solutions force data users to manually
provide the contextual information for encrypting or decrypting data. This way
indicates that malicious data users can conjecture which attributes are
incorrect, such that it is possible to raise unauthorized access to
information. Ghosh et al. [71] propose a context-aware attribute learning
scheme CASE for tackling the issues. Depending on the edge intelligence, CASE
can learn users’ contextual attributes and generate attributes automatically,
which could decrease manual operations and thus enhance the system’s security.
Meanwhile, it reduces the size of post-encryption data based on learned
attributes, which increases the scalability of access control as discussed in
the experiments. Because a trust degree has regional characteristics in edge
computing, Zheng et al. [72] propose an access control scheme based on CP-ABE,
which combines users’ trust degree. The scheme leverages the classification
and recommendation algorithms for adaptively determining users’ trust degree
in different regions. Therein, the trust degree is divided into three
dimensions: the basic trust degree, the behavior trust degree, and the
recommendation trust degree. The number of attributes in the scheme is
constant, which decreases the computational overhead.

TABLE III: General access control schemes in data storage

Ref | Base Model | Requirements |  Performance |  Key Improvements
---|---|---|---|---
[66] | CP-ABE |  RS1&RS2 |  Computation cost: Key generation ($2N_{A_{u}}$+$\left|\mathbb{A}\right|$)$T_{G}$, Encryption ($3N_{A_{u}}$+2(n-$\left|\mathbb{P}\right|$))$T_{G}$ \+ $T_{G_{T}}$, Decryption ($\left|\mathbb{A}\right|$-$\left|\mathbb{P}\right|$)$T_{G}$+$N_{A_{p}}$$T_{G_{T}}$+$3N_{A_{p}}$$T_{p}$. Ciphertext size: $3N_{A_{p}}$$\left|G\right|$+2$\left|G_{T}\right|$, Key size: $\left|Z_{p}\right|$. |  The size of the secret key is kept irrespective of the number of attributes and the ciphertext size is agnostic for the number of attributes and has a linear relationship with the number of authorities
[67] | KP-ABE |  RS1&RS2 |  Computational complexity: Encryption $\mathcal{O}$($\mathcal{S}$+$\mathcal{R}$), Decryption $\mathcal{O}$($\mathbb{R}$). Space Complexity: Encryption key $\mathcal{O}$($\mathcal{S}$), Decryption key $\mathcal{O}$($\mathbb{R}$). |  Filtering useless data in the ciphertext for IoT devices (as data receivers) according to more constraints of data producers and consumers.
[68] | CP-ABE |  RS1&RS2 |  Computation cost: Encryption (4$N_{r}$+1)G, Decryption [at edge] ($D_{a}$+1)G, Decryption [at end device] G. Ciphertext size (3$N_{r}$+1)$|G^{{}^{\prime}}|$, Public key size (2n+2)$|G^{{}^{\prime}}|$, Private key size $|\mathbb{P}|$ $|G^{{}^{\prime}}|$. |  Cutting down the computation burden for resource-limited IoT devices by changing the mathematics in cryptography.
[69] | CP-ABE |  RS1&RS2 |  Computational complexity: Key generation $\mathcal{O}$(1), Encryption $\mathcal{O}$($\mathcal{S}$+$\mathbb{R}$), Decryption $\mathcal{O}$($1$). |  Reducing the computation burden for resource-limited IoT devices from multiple aspects.
[70] | CP-ABE |  RS1&RS2 |  Storage overhead: $(\left|\mathbb{A}\right|+2)\left|{G}_{1}\right|$ , Communication overhead: 359 bytes (IoT gateway), 16 bytes (Smart thing), Computation cost: 673.4ms (IoT gateway), 0.1 ms (Smart thing) |  Introducing context into CP-ABE and providing dynamic access control.
[71] | CP-ABE |  RS1&RS2 |  The study mainly evaluates average network delay, average energy consumption, and average packet loss. In each metric, CASE has significant improvement by about 33%. |  Introducing edge intelligence to avoid manually providing context information and sensitive data inference.
[72] | CP-ABE |  RS1&RS2 |  Due to the three-levels trust degree, the number attributes of is constant. |  Depending on AI to determine the trust degree of users in different regions adaptively, and combining CP_ABE with the trust degree.

  * 1

Table IV provides descriptions for various notations.

TABLE IV: Notations Notation | Description
---|---
AAs | attribute authorities
n | Total number of attributes in the system
$\left|\mathbb{P}\right|$ | Number of attributes in access policy $\mathbb{P}$
$\left|\mathbb{A}\right|$ | Number of attributes in user’s attribute set $\mathbb{A}$
$N_{A_{p}}$ | Number of AAs whose attributes are used in policy $\left|\mathbb{P}\right|$
$N_{A_{u}}$ | Number of AAs whose attributes are used in user’s attribute set $\left|\mathbb{A}\right|$
$T_{G}$ | Time to execute an exponentiation operation in an element of source groups $G_{1}$ and $G_{2}$
$T_{G_{T}}$ | Time to execute an exponentiation operation in an element of target group $G_{T}$
$T_{p}$ | Time to execute one pairing operation
$\left|Z_{p}\right|$ | Size of an element of $Z_{p}$
$\left|G\right|$ | Size of an element of source group $G_{1}$ and $G_{2}$
$\left|G_{T}\right|$ | Size of an element of target group $G_{T}$
$\mathcal{S}$, $\mathcal{R}$ | Attribute sets of a sender and a receiver
$\mathbb{S}$, $\mathbb{R}$ | Policies associated with a sender and a receiver
$N_{r}$ | Number of rules in $\left|\mathbb{P}\right|$
Da | Minimum number of attributes satisfying the access policy
$|G^{{}^{\prime}}|$ | 160 bits considered as a unit of measurement

The aforementioned studies provide general considerations for enhancing the
performance of ABE in edge computing. Apart from these general considerations,
we should have some access control schemes for specific domains. We next
simply present some studies in transportation and healthcare domains.

In the realm of transportation, edge computing is vital for conducting data
access with low latency, and ABE-based access control is researched
correspondingly. For example, OpenVDAP [73], an open full-stack edge
computing-based platform, is dedicated to achieving low-latency data analytics
for connected and autonomous vehicles (CAVs). Vehicle sensing data include
historical data and real-time data, and different data sources have different
access patterns. AC4AV [74] first defines the data access control problem in
CAVs. AC4AV can support various access control models (including ABE) by
allowing users to dynamically adjust and customize access control models.
AC4AV is implemented based on the OpenVDAP platform. To address the security
and privacy issues in car-hailing systems, Sun et al. [75] propose a fine-
grained puncturable matchmaking encryption (FP-ME) based on MABE and
puncturable encryption. In FP-ME, not only the fine-grained access control
between passengers and drivers about orders can be achieved, but also, the
privacy and authenticity of passengers’ orders are protected and the
timeliness of passengers’ ciphertext orders is guaranteed. To achieve secure
data sharing in intelligent autonomous vehicle platoons, Sun et al. [76]
initially propose a privacy-preserving data share mechanism with flexible
cross-domain authorization (FDSM-FC). In FDSM-FC, the privacy-preserving
matchmaking encryption technology is combined with the cross-domain
transformation primitive. In this way, it exploits the dual merits of those
technologies and achieves data authenticity and authorization simultaneously.
In addition, Bao et al. [77] present an EA-PH-ABE-DS scheme in the Internet of
Vehicles (IoV) scenario where data services are expected to be more flexible
and diversified. EA-PH-ABE-DS efficiently supports the update of ciphertext
and user secret key in IoV and leverages edge computing to reduce computation
overhead further.

In the health care domain, Zhang et al. [78] design a lightweight and fine-
grained access control for electronic medical record sharing systems. Therein,
symmetric encryption is used to encrypt medical records by data owners and ABE
is used to protect symmetric key. Hassan Nasiraee and Maede Ashouri-Talouki
[79] propose a new privacy-preserving distributed data access control (PDAC).
In PDAC, a new user anonymity approach, a novel policy-hiding mechanism, and
an independent authority system are simultaneously considered for improving
privacy preservation. The anonymity approach and policy hiding can prevent
untrusted authorities from colluding, and the authority system adds/removes an
authority without reinitializing other authorities.

###  V-B Summary and Lessons Learned

In this section, we have discussed the studies to satisfy RS1&RS2. Some
approaches are summarized in Table III for the discussion, and learned lessons
are as follows:

  * •

Rational use of computing power: In these research studies, resource-
constrained IoT devices leverage the computing powers from third parties
(e.g., edge nodes and cloud) to offload the computing load from cryptography
algorithms. Most works simply and rigidly outsource the whole computing tasks
by neighbor edge servers. The outsourcing model indicates that IoT devices
need neighbors with relatively ample resources. However, the neighbors may
have different computing resources. For example, general CPU-based edge
servers can achieve outsourced computing tasks. Also, ASIC is designed for
accelerating data encryption/decryption [80]. Moreover, the network from IoT
devices to neighbors changes randomly. For example, edge servers
intermittently offline, and large traffic induces network congestion [81]. It
is advisable to have more flexible task-offloading strategies for cryptography
algorithms, which could improve the availability of computing power. Such
that, resource access can be controlled more efficiently in pervasive edge
computing by more rational use of computing power.

  * •

Reduction in computational power requirements: Larger key size and ciphertext
size need more computing resources regardless of the usage strategies of
computing power. Some research studies focus on improving the performance of
cryptography-based schemes by cutting down these sizes or making them
constant. However, these solutions reduce the expressive capability of access
policy, i.e., the system’s ability to satisfy ample security requirements is
declined. We should consider a trade-off between policy expressiveness and
data size. Besides, reducing the requirement for computing power appropriately
is labor-intensive and expert. It could facilitate the development of
cryptography-based access control if we make that trade-off easier for general
engineers.

  * •

Introduction of additional access control components: In different application
scenarios, various components for access control are utilized, including
bilateral practitioners, trust, context information, etc. For example, edge
nodes and cloud are bilateral practitioners for determining which IoT devices
can access stored data. In this way, access policy is considered with more
components, as a result, more useless data can be previously filtered for IoT
devices by more constraints. The trust of data users is a factor for
dynamically determining whether encrypted data can be decrypted. Context
evaluates whether the current time or location is satisfied for decrypting
data. In edge computing, we cannot sometimes pre-know which components are
suitable for which scenario. Automatically recognizing components for specific
scenarios can facilitate data security in pervasive edge computing.

  * •

Reduction in human power: In the studies mentioned above, some use machine
learning/deep learning to substitute human power for achieving functional
components in access control systems. In this way, the components, which
conventionally need human power for maintenance, can be optimized enough, and
as a result, the access control system can be more efficient. For example,
Ghosh et al. [71] largely reduce the average network delay, energy
consumption, and packet loss by integrating context-aware attributes learning
scheme into CP-ABE. In addition, data privacy in the system could be improved
due to less human involvement. Machine learning-based access control should be
researched further to introduce effective and efficient intelligent algorithms
into access control schemes.

  * •

The transportation and healthcare domains involve many sensitive data. When we
design the access control schemes in the end-edge-cloud architecture in these
domains, the privacy-preserving methods should be considered thoroughly.

TABLE V: Comparison among access control schemes in edge computing from data usage Scheme | Centralization | Obligation | Purpose | Monetization | Granularity | Requirements | Application
---|---|---|---|---|---|---|---
[82] | ✓ | ✓ | ✓ | ✓ |  Spatiality, Temporality, and Aggregation |  RU1&RU2 | Smart cities
[83] | ✓ | ✓ | ✓ | ✓ |  Temporality and Aggregation |  RU1&RU2 | Food industry
[84] | - | ✓ | - | - |  Temporality |  RU1&RU2 | General
[85] | - | - | ✓ | - |  Temporality |  RU1&RU2 | Automotive industry
[86] | ✓ | - | ✓ | - |  Temporality and Aggregation |  RU1&RU2 | General
[87] | ✓ | - | ✓ | - |  - |  RU1&RU2 | General

##  VI Access control in data usage

After the access control mechanisms in data collection and storage grant
access to data, their responsibility for protecting data would end. However,
some data owners could expect to learn about how their outsourced data is used
and control their data usage. Data usage control could ensure data sovereignty
for data owners. We next present the recent efforts in DUC.

Generally, DUC involves centralized and decentralized implementations. Cao et
al. [82] implement a centralized data usage platform for smart cities, where
data usage is restricted by data providers, data monetization is allowed,
various obligations are considered, and the used/processed data can be
visualized. Therein, the user’s role can be used for authorizing data
monetization (RBAC). As shown in experiments, the end-to-end delay, i.e., the
time from processing the data consumer request to getting the data response,
is increased by 42 ms on average. The time of processing usage control is
increased by 36 ms on average. Similarly, Munoz-Arcentales et al. [83] present
a centralized implementation of DUC for securing data sharing among multiple
organizations in the industry. Kelbert et al. [84] formalize, implement, and
evaluate a fully decentralized system of DUC, where data flow can be tracked,
and data usage policies can be propagated and enforced effectively. The
assessment indicates that 1) dataflow tracking and policy propagation achieve
21%–54% of native execution throughput, and 2) decentralized policy
enforcement generally outperforms centralized methods.

Traditionally, the DUC mechanism pre-defines data usage pipelines across
domains, e.g., IoT-generated data is outsourced to edge servers for advanced
analysis. However, this fashion complicates the process of DUC policy
specification, especially when the data usage pipeline is mutable. The
existing DUC techniques cannot preventively and proactively enforce data usage
control. Cirillo et al. [85] propose a practical distributed DUC framework
IntentKeeper based on the open-source IoT edge computing framework FogFlow
[88]. It decouples data usage flow with data usage policy and allows users to
specify DUC policy easily using an ODRL-like model [89]. And, it supports
preventive and proactive enforcement by federated service orchestration and
access control data management. Use case validations demonstrate that
IntentKeeper reduces the complexity of policy specification by up to 75% in
moderately complex scenarios, compared with current flow-based approaches.
Additionally, experimental results indicate that IntentKeeper ensures rapid
response times (under 40 ms) with minimal overhead (below 10 ms).

To enforce access control on the general big data computing platform, Spark,
Xue et al. [86] propose purpose-aware access control by enhancing purpose-
based access control with data operation/processing purpose (describing the
fine-grained data usage). They implement purpose-aware access control in the
execution and optimization layers. As a result, the SQL query, machine
learning algorithm, streaming computation, and graph computation can be
automatically supported by this solution, meanwhile introducing acceptable
latency for big data analytics. The fine-grained purpose and the
implementation methodology can be adapted for other data analytics at the
edge. In addition, IntentKeeper [85] supports vehicle analytics-related
purposes for enforcing DUC.

Arora et al. [87] use the higher-order relationship context information to
control data usage in the dynamic environment in IoT applications. This scheme
uses the history of relationship changes to make authorization decisions. This
means that the relationship temporality should be considered. For example, if
a nurse has already used the medical data of Alice for care, this nurse cannot
use the medical data of Bob for medical research purposes. The proposed graph-
matching algorithms for authorization are effective and efficient as shown in
the experiments when temporality is introduced into ReBAC.

Summary and Lessons Learned: In this section, we have discussed the studies to
satisfy RU1&RU2. The approaches are summarized in Table V, and the learned
lessons are as follows: Most schemes focus on a specific application realm to
implement centralized DUC by building the corresponding purpose set.
Obligation is considered in most DUC implementations. DUC can use monetization
to facilitate data flow. The granularity of DUC can be divided into three
categories: spatiality, temporality, and aggregation. Different schemes may
involve different granularity. The purpose component is regarded as the core
component for satisfying data owners’ privacy in DUC [21]. In purpose-based
schemes [90], data owners specify “which data users can use data in which
purpose”. If the usage purpose indicated by data users complies with the data-
owner-specified purpose, the user’s query will be allowed to use data.
Purpose-based schemes control data usage before execution, meaning that the
data analysis logic operates on data loaded into memory in processors (e.g.,
CPU).

##  VII Blockchain-based access control platform

Although the studies in the above sections can be selected for achieving
access control, which depends on small resources or has acceptable delay,
flexibility, and scalability, they do not consider the security of critical
access control data in edge computing. In this section, we study how to ensure
this security by depending on the advantages of blockchain, i.e.,
decentralization, immutability, programmability, and security [32, 33, 34]. We
introduce the recent blockchain-based access control platforms developed for
managing the data of access control in different data lifecycles. In
particular, we mainly introduce which typical access control models are
combined with blockchain, and the method of exploiting blockchain to manage
the data of these models.

###  VII-A Blockchain-based access control in data collection

Liu et al. [91] introduce an access control system, Fabric-IoT, which exploits
Hyperledger Fabric to manage ABAC used for the resource data generated by IoT
devices. They use smart contracts to implement resource access, policy
management, and policy enforcement. In particular, the device contract
provides methods to store/query URLs of resource data; the policy contract
provides functions to manage access control policies; and the access contract
enforces access control. The simulation experiments present acceptable
throughput and efficient consensus.

Mazzocca et al. [92] propose a risk-aware authorization middleware for
healthcare based on federated learning. Blockchain stores the trained
federated model, which evaluates the risk level of the current patient. Sylla
et al. [93] propose a new blockchain-enabled decentralized context-aware
authorization system. Users can dynamically define contextual access rights
for each IoT application in smart contracts. When an authorized client issues
a request, the smart contract generates the corresponding contextual access
token. With the edge deployment of blockchain, the maximum generation time of
contextual access tokens is about 10 ms.

In practice, we need the management of capability registration, delegation,
and revocation in CapBAC. The centralized management component likely induces
performance bottlenecks and single-failure problems. The blockchain can be
used for capability registration, delegation, and revocation [94, 95]. The
blockchain-enabled decentralized CapBAC (BlendAC), which is being researched
in the academic community, can avoid those problems. Xu et al. [96] propose a
BlendAC for large-scale IoT systems. It achieves robust identity-based
capability token management, where the smart contract is used for
registration, validation, delegation, and revocation. They implement BlendAC
on a Rasberry Pi device and on a local private blockchain. The experimental
results demonstrate that BlendAC can offer decentralized, scalable,
lightweight, and fine-grained access control. Similarly, the following studies
design the blockchain platforms based on CapBAC, where different capability
tokens are considered. IoT consortium capability-based access control (IoT-
CCAC) [97] utilizes blockchain to achieve decentralized CapBAC schemes for
consortium applications in IoT cities, where IoT-generated data from different
parties is governed for different services by using group capability tokens.
Nakamura et al. [98] propose a novel CapABC scheme, where the smart contract
is used for managing the tokens that each combines with an action and are
different from the conventional token model, i.e., one token per subject. This
solution allows subjects to have multiple tokens and facilitates flexible
token delegation among multiple subjects. Chen et al. [99] present a fine-
grained and flexible access control scheme, which simultaneously achieves
flexible capability granting and revocation, sufficient capability
granularity, secure capability delegation, and capability verification. This
scheme combines blockchain with CapBAC, where a smart contract manages subject
and object information. It designs authorization rules and a tree for
capability, where the token considers context information.

###  VII-B Blockchain-based access control in data storage

Considering the difficulty of deploying the existing blockchain platforms,
Zhang et al. [100] design a lightweight blockchain (LBC) by improving the
proof-of-work consensus for lightly implementing blockchain. Moreover, they
use blockchain to store access control policy to protect the privacy of policy
and design six smart contracts for integrating ABAC and ABE. In particular,
the policy management contract (SCPA) operates the policy set and generates
encryption policies; the policy decision contract determines the policy
compliance according to policies from SCPA; the policy information contract
manages attribute information for SCPA; the encryption and decryption contract
enforces ABE by edge nodes; the penalty mechanism contract manages the
punitive measures for illegal access; the policy enforcement contract returns
the encrypted data or available resources. Attribute updates/revocations in
ABE are incompatible with the immutability of blockchains. To address this
issue, Yu et al. [101] propose a new blockchain-based access control for IoT
systems, which allows attribute updates/revocations by integrating Chamelon
Hash (CH) algorithms into blockchains. To ensure the security of key updates,
they further design a key chain for verifying key updates to avoid the abuse
of CH update function. However, these works do not consider the privacy
preservation of user data. Han et al. [102] propose a blockchain-based
auditable access control system for private data in IoT. Based on the ABAC
model, they define a model to divide the IoT data into private data and public
data and utilize smart contracts for storing and managing the private data,
access control policy, and access records. In the smart grid area, Yang et al.
[103] present a blockchain-based CP-ABE access control system for smart grid,
where amounts of data generated by IoT devices need to be shared among
distributed energy resources, microgrids, and the main grid. The blockchain is
combined with edge computing, where the partial encryption/decryption tasks of
CP-ABE are executed in the form of smart contracts.

###  VII-C Blockchain-based access control in data usage

IntentKeeper [85] uses Hyperledger Fabric to propagate, synchronize, and
record some important data, e.g., data usage policies, distributed sites, and
policy enforcement decisions. Syed et al. [104] design a blockchain-based
vehicle lifecycle tracking framework, where Hyperledger Fabric enables the DUC
engine to monitor vehicle maintenance, insurance, and rented vehicles
continuously (e.g., restriction on the number of vehicle leases).
Specifically, they develop smart contracts for enforcing DUC and maintaining
DUC policy. Wu et al. [105] present the TS-PBAC model, which leverages
blockchain-enabled secure logs to evaluate quality data from the external
network. This model allows individual-centric privacy preservation in the
Internet of Medical Things scenarios, where medical data can be specified for
different purposes by data owners. Meanwhile, local differential privacy and
RBAC are combined to protect sensitive attributes against internal users. To
mitigate the trust and security problem for IoT big data management, Ma et al.
[106] propose a BlockBDM scheme, where public and permissioned blockchain is
utilized for performing data operations and their management. In particular,
BlockBDM achieves secure usage control by limiting the operations (e.g., data
backup and data save) and recording data usage for auditing trails. These
studies [107, 108, 109, 110, 111] combine trusted execution environment (TEE),
e.g., SGX, with blockchain. The smart contract is used to manage data usage
policies and data trading prices. Controlling data usage is achieved by a TEE-
based off-chain contract execution engine, and the results are securely
committed to the blockchain for the auditing trail.

###  VII-D Blockchain-based versatile access control platform

A blockchain-based versatile access control platform can provide multiple
access control implementations, which intends to be used for multiple data
lifecycles. Implementing a useful versatile access control platform is tricky
because data and services are complicated in edge computing, the application
scenarios cannot be pre-known, and on-chain data is costly [112]. Focusing on
the smart contract-based access control (SCAC), Zhu et al. [113] explore how
to combine blockchain security with fine-grained access control models while
keeping the efficiency of edge computing. SCAC depends on ABAC and RBAC to
develop basic entities in the model of SCAC, including attributes (e.g., token
and identity), smart contracts (e.g., register manager and device, set
attribute predicate, and query information). Ouaddah et al. [114] present a
blockchain-based access control framework FairAccess to enable its users to
own and control their data. It uses a public Bitcoin-like blockchain to act as
the access control manager, which supports various access control models
(e.g., RBAC and ABAC) and transactions (grant, get, delegate, and revoke
access). In [115], they update FairAccess to FairAccess2, where more granular
data access control policies are supported based on Ethereum. Based on
Ethereum, Oscar et al. [116] propose a decentralized access control system for
the IoT. This system utilizes blockchain technology to store and distribute
access control information, and it supports resource-constrained devices. A
single smart contract is designed to enforce access control based on the
access control policy of a set of IoT devices, where a manager is supervised
to manage these devices. However, this access control approach is too complex
for changing permissions and cannot fully meet the dynamic requirements of the
IoT. Zhang et al. [117] present a novel framework for the IoT that centers
around Access Control Contracts (ACCs). In this framework, each ACC is
responsible for defining a specific access control method based on a static
access control policy. Simultaneously, a Judge Contract dynamically evaluates
the ACCs by assessing the behavior of subjects registered in the system. The
Register Contract records access control information, misbehavior-judging
methods, and the associated smart contracts, offering functionalities such as
registration and updates. This approach provides a structured and dynamic
means of managing access control within IoT environments.

###  VII-E Summary and lessons learned

In this section, we have discussed the studies applying blockchain to protect
important access control data while satisfying RC1&RC2, RS1&RS2, or RU1&RU2.
The approaches are summarized in Table VI, and the learned lessons are as
follows:

TABLE VI: Comparison among blockchain-based access control schemes in edge computing for data collection, storage, and usage Lifecycle |  Scheme |  Blockchain |  Supported model(s) |  Operations
---|---|---|---|---
Data collection |  [91] |  Hyperledger Fabric |  ABAC |  Policies CRUD, policies enforcement, resource URLs CRUD
|  [92] |  Ethereum |  RAAC |  Stores the trained federated model
|  [93] |  Hyperledger Fabric/Ethereum |  CAAC/CapBAC |  Context token register, validation and revocation
|  [96] |  Ethereum |  CapBAC |  Identity token register,validation, delegation, and revocation
|  [97] |  BigchainDB |  CapBAC/GBAC |  Group token register,validation
|  [98] |  Ethereum |  CapBAC |  Action token register, validation, delegation, and revocation
|  [99] |  Hyperledger Fabric |  CapBAC/CAAC |  Token register, validation, delegation, and revocation
Data storage |  [100] |  LBC |  CP-ABE/ABAC |  ABAC policy CRUD, and encrypting/decrypting data or policy file by CP-ABE
|  [101] |  - |  CP-ABE |  Attributes update/revocation
|  [102] |  Hyperledger Fabric |  ABAC |  Upload/Query private data and URLs of public data, Update/Query access record, Policy CRUD
|  [103] |  Hyperledger Fabric |  CP-ABE |  Data owners and data users can use smart contracts to accomplish partial data encryption and decryption
Data usage |  [85] |  Hyperledger Fabric |  DUC |  Upload/Query data usage policies, distributed sites, policy enforcement decisions.
|  [104] |  Hyperledger Fabric |  DUC |  Upload/Query data usage policies and attributes, DUC enforcement engine
|  [105] |  ChainSQL |  DUC |  Record EMR records, model parameters of access control, privacy policies, and access logs. Enforce PBAC
|  [106] |  Hyperledger Fabric |  DUC |  Limit the number of uses, the period of use, backups, etc.
Multiple lifecycles |  [113] |  Hyperledger Fabric |  RBAC and ABAC |  Register manager and device, set attribute predicate, query information, etc.
|  [114, 115] |  Modified Bitcoin, Ethereum |  RBAC, ABAC, etc. |  Grant, get, delegate and revoke access
|  [116] |  Ethereum |  General architecture for resource-constrained devices |  Register manager and device, add and remove manager, add access control enforcement, revoke permission, etc.
|  [117] |  Ethereum |  General architecture for resources access in IoT |  Register access control information, misbehavior-judging method and corresponding smart contract, and provides operations (e.g., register and update)

  * •

In data collection, many studies focus on utilizing blockchain technology to
optimize traditional CapBAC by implementing decentralized CapBAC. The smart
contract generally accomplishes operations on tokens in CapBAC. As lessons
learned in Section IV-A, the tokens in CapBAC are often incorporated with some
components (e.g., trust and group). As such, it would be interesting to build
a blockchain management system, which can provide a library of various tokens
for complex edge computing applications.

  * •

In data storage, blockchain can be used to manage access policies in ABE,
execute computing in ABE, record access permissions, etc. Depending on a
blockchain, we can integrate ABAC and ABE securely while achieving flexible
and scalable access control.

  * •

Blockchain in each DUC scheme is generally used as a platform to regulate data
usage rules, guarantee the consensus of executing data usage rules, and record
model parameters and data usage records. Purpose-based schemes generally
request data users to input their data usage purpose. In this fashion,
malicious users may cheat purpose-based enforcers. Relational audits can deter
data users from being dishonest if the purpose-based schemes are integrated
into blockchain to manage purpose.

  * •

From the blockchain for access control in data collection, storage, and usage,
we can observe that each data phase has a limited number of blockchain-
supported models. Given the intricate and dynamic nature of the access control
requirements, each data phase should be equipped with a sufficient range of
blockchain-supported models. In addition, we observe that the access control
schemes in each data phase have common components, such as model parameters
management, policy CRUD, access logs record, attributes CRUD, model
enforcement, etc. We could design a lightweight blockchain-based versatile
access control platform depending on these common components. Finally, these
blockchain-based approaches are crafted for distinct scenarios individually,
involving Hyperledger Fabric, Ethereum, ChainSQL, etc. If each separated
blockchain in each data phase integrates appropriately, it could induce extra
overhead, such as communications between different access control systems of
multiple data phases, and prevent access control performance from being
improved in edge computing. The interaction among different blockchains should
be supported for access control systems.

  * •

Blockchain can support multiple access control models for multiple lifecycles,
where smart contracts enable various operations: register access control
enforcement, manage attributes, etc. Versatile access control technology could
be the feasible solution to solve the data security problem in edge computing,
which has various stakeholders, ample data sources, diversified dynamics, and
complex applications. However, the current blockchain-based versatile access
control platforms mainly support conventional models (e.g., ABAC, RBAC, and
CapBAC), and lag the development of new techniques as introduced in Section
IV&V&VI, where the improvements for conventional models are designed.

##  VIII Challenges and Future Research Directions

Apart from the aforementioned issues, there remain challenges and new research
directions in edge computing access control to be discussed, as follows.

###  VIII-A Machine learning-based access control in edge computing

Traditional access controls commonly depend on predefined rules and require
laborious maintenance and updates. With the widely distributed resources in
edge computing, the demand for a more dynamic and efficient access control
system is escalating. Machine learning can play an important role in
satisfying the demand [118], where trained machine learning models constitute
some blocks in access control systems to automate and reduce labor. Some
studies use machine learning technology in their access control systems, e.g.,
[58, 72, 71]. We would discuss and envisage future research directions in the
following aspects.

  * •

Limited datasets: A complete access control policy is the prerequisite for
effective data protection. The natural language documents for access control
requirements are essential sources of access control policies [119]. Natural
language processing technology is the common tool for extracting access
control policies from those documents. It identifies and analyzes policy
information described in human language, and converts this information into
structured data that computers can comprehend and process. For example,
Freisleben et al.[120] present an approach based on K-nearest neighbors
algorithms for clustering ABAC policies, which can reduce the number of policy
rules by evaluating similarity. Text2Policy [121] classifies sentences into
four access control modes. If a sentence matches, it extracts the subject,
action, and object from the sentence by using the comments of the sentence.
Text2Policy does not support contextual information extraction, nor does it
support complex sentence patterns. To overcome the above defects, Narouei et
al. [119] improve semantic role labeling and introduce domain-specific
knowledge to greatly improve the performance of extracting implementable
policies from natural language access control policy documents. The above work
can support RBAC, ABAC, and Access Control Lists, but they do not support DUC.
Hosseinzadeh et al. [122] systematically propose a method to extract
implementable policies from data usage requirement documents from the
perspectives of data owners: policy declaration, policy transformation, and
policy negotiation.

Limitation: These studies facilitate the development of techniques for
extracting access control policies from natural language documents. However,
the explored datasets are mostly from conventional domains such as education
and medicine. The lack of datasets from edge computing, especially in IoT,
could impede the development of its access control technologies. Besides,
real-world access control policies generally involve sensitive data, which
obstacles the development of datasets.

Possible Solutions: Policy generation can mitigate the limitation [123]. The
existing approaches to generating policy are problematic, e.g., random policy
generation and manual policy writing. The datasets created using these methods
are idealized, and do not fully capture the complex authorization scenarios in
the real world. To generate simulated data with a similar distribution to any
realistic data, we can explore pre-trained models (e.g., GPT4 [124]), such
that the study of machine-based access control can reduce the reliance on
real-world open-source datasets. The following aspects should be considered:
1) access control models, 2) application domains, and 3) different neural
networks for data simulation. Different access control models have different
organizations about access control information so the pre-trained models have
different performances on different models. Many applications in IoT, e.g.,
the Internet of Vehicles and smart homes, have different data flow and
business parties. Different application domains require different data
simulations. In large-scale access control scenarios, business parties can
leverage pre-trained models to simulate data, test the access control
functions overall beforehand, and monitor access control systems timely.
Different neural networks require different computation power and could have
different performances for various access control models and application
scenarios. To make a cost-effective data simulation, business parties should
select appropriate pre-trained models.

  * •

Federated learning: Different business organizations in the edge computing
ecosystem would form alliances based on business interests. The aforementioned
studies in data lifecycles can be used intra-alliance, in which data owners
and consumers cooperate to exert IoT-generated data. Due to data owners’
limited data protection ability, their access control policies may be
incomplete. These data owners can use the access control policies of other
data owners to enhance their access control policies, such as adding missed
access control policies or updating inferior access control policies. For
example, Abu Jabal et al. [125] propose FLAP, a federal learning framework for
ABAC. It allows data owners to learn ABAC policies from each other, ensuring
that each party generates accurate policies. Moreover, FLAP takes into account
policy conflicts and privacy concerns. To solve the problem of insufficient
access control policies in a single smart home IoT context, Yu et al. [126]
propose the federal multi-task learning framework LoFTI for several federated
smart homes. The framework ensures the privacy of the access control policy in
a smart home, enhances its context-based access control policy, and realizes
the personalization of the smart home policy. Federated learning could be
feasible in the cross-domain optimization of access control policies. However,
they have limited consideration of the complex data lifecycles in edge
computing. Multi-data lifecycles indicate access control policies of multiple
access control models. An interesting research direction is to optimize each
data owner’s access control policies of multiple access control models,
meanwhile without privacy leaking among data owners. A possible solution could
be versatile federated learning, targeting a multi-access control federated
model, which allows data owners to enhance access control policy with less
consideration of the access control models.

  * •

Adversarial attacks: Although machine learning-based access control systems
have the potential for enhancing data security in largely distributed edge
computing, the deployment of such systems can be hampered by the vulnerability
of machine learning models to adversarial attacks. These attacks exploit
weaknesses in machine learning algorithms to violate the system’s access
control policies to gain unauthorized access. For example, Nobi et al. [127]
propose that adversarial attacks can be reduced to a certain extent by
utilizing specific constraints on access control. We would further explore
solutions for implementing machine learning-based access control and
mitigating various attacks within the edge computing ecosystem. These
solutions should consider the characteristics and features of access control
in edge computing, such as various models and large scale.

###  VIII-B Hybrid access control

Hybrid access control technology can improve access control performance by
combining the advantages of different access control mechanisms [128]. In edge
computing access control, the introduced models in Section II, e.g., CapABC,
ABE, and DUC, are a set of data security techniques. As shown in Figure 5, we
can combine these models’ components to enhance the performance of access
control in edge computing. For example, to satisfy the low-latency
requirements and secure access control systems for the Industrial Internet-of-
Things (IIoTs). DHACS [129] combines these elements: role, context, and
organization regulation. The functions of access control are encoded in smart
contracts. The operational transactions are recorded on the blockchain. The
efficiency of access control management is improved by about 30%. DHACS is the
first attempt to use a decentralized blockchain with smart contracts for
hybrid access control in IIoTs. Because services involve data analytics, it is
reasonable to introduce appropriate components of DUC into CapBAC to optimize
access control performance further. The components of purpose-based schemes
can be applied to ABE-based access control, which could further restrict data
usage in advance and reduce the computing cost of decryption by purpose.

Figure 5: Hybrid access control.

Hybrid schemes’ effectiveness, efficiency, and flexibility can be researched
for high-performance edge computing access control under pervasive data
analytics. For example, the method to make a more flexible CapBAC should be
inspected when CapBAC is combined with DUC. The method to implement efficient
decryption/encryption could be investigated when the purpose attribute is
integrated into the ABE scheme.

Finally, while hybrid access control can improve performance, privacy issues
in hybrid models should be investigated because hybrid means that some cases
involve organization-across attributes.

###  VIII-C Powerful access control technology testbed

Some testbeds in the mentioned studies use simulation tools to verify their
ideas. Some deploy complex real end-edge-cloud platforms, which enable the
tests to be closer to real production scenarios. The new paradigm, i.e., edge
computing [81], has complex network topology (e.g., LAN, MAN, and WAN) and
heterogeneous network devices (e.g., Cisco or Huawei switch). Meanwhile, IoT
devices and edge nodes are heterogeneous with different computing, storage,
and communication capabilities. Except for the sophisticated hardware
conditions, heterogeneous IoT and network protocols coexist. For example, MQTT
and XMPP are usually used to collect IoT-derived data in a certain security
domain. WiFi and 5G enable devices to connect with and communicate in
networks. Because this physical ecosystem has enormous economic expenditures
and complex maintenance, it is difficult to achieve flexible and extendable
tests for research and real production scenarios.

To tackle these challenges, many open-source simulation tools for edge
computing are developing [130, 131, 132], which prompts the development of
edge computing technology. They can simulate common infrastructures, including
networks, mobile devices, edge data centers, microservices, edge
orchestrators, edge clusters, and central cloud. Moreover, they support
various computing loads, different data distributions, and access to real
data.

These simulation systems do not consider the simulation of access control
currently natively. However, they support powerful module customization and
extension. These existing simulation systems can be extended to support access
control simulation. To achieve this simulation in an edge computing ecosystem,
similar to supporting various network protocols, these simulation systems
should flexibly integrate heterogeneous access control models/protocols. For
example, the attribute authorities and context information should be allowed
according to the user’s configuration in the simulation system. It is not
advisable to embed rigid access control models into simulation systems
according to real experiences about applications, because the applications in
edge computing are various and could be changed frequently. To integrate
different access control models flexibly, we need a universal library for
various model modules, each of which acts as a basic building block for
constructing a functional model template. In addition, real data should be
flexibly and advisably injected for the corresponding function verification in
the light of real-world situations. Because access control would run on
enormous data in real situations, capturing all real data in a simulation
system is impractical. The trade-off between the amount of ingested data and
verification accuracy should be researched, especially considering diversified
data categories in edge computing. Finally, the high usability of the
simulation system would be an important problem to be solved, so that the
system users can build the target access control system efficiently. As such,
researchers or technicians can focus on verifying their ideas for implementing
access control functions.

##  IX Conclusion

This paper has examined access control models applicable to edge computing,
highlighting their critical role in enabling low-latency, IoT-driven
applications like smart homes and autonomous vehicles. Systematically
classifying these models and evaluating their effectiveness across different
data lifecycles could assist readers in understanding access control
technologies in edge computing, where the unique challenges include limited
resources, the need for low-latency processing, dynamic interactions, and
decentralized architectures. Furthermore, the paper has effectively
illustrated the potential of blockchain technology as a robust solution to
enhance security and integrity within access control systems. The
decentralized and immutable characteristics of blockchain are particularly
beneficial in reinforcing the security measures necessary in distributed edge
computing settings, where traditional access control mechanisms often fall
short. Through thoroughly exploring existing access control models and their
adaptations to the unique demands of edge computing, the paper not only
presents its readers with the state-of-the-art but also emphasizes the ongoing
necessity for innovation. To match the fast-evolving IoT and edge computing
landscape, we should create more adaptive, efficient, and secure access
control solutions. Continued research is essential to fully leverage edge
computing while maintaining data security.

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Tao Xue received the Ph.D. degree from University of Chinese Academy of
Sciences, Beijing, China, in 2022. He is currently a postdoctoral researcher
with Xidian University. His research interests include access control, data
security, edge computing, machine learning, and the Internet of Vehicles.
---
Ying Zhang is currently working toward the MS degree with the Hangzhou
Institute of Technology, Xidian University, Zhejiang, China. Her research
interests include access control, federated learning, and the Internet of
Vehicles.
---
Yanbin Wang graduated from Zhejiang University with a Ph.D. in Computer
Science and Technology. He is currently an Associate Professor at the Xi’an
University of Electronic Science and Technology’s Hangzhou Research Institute.
His research interests lie in AI4Security, with a focus on large-scale
multitask learning in cybersecurity. He has authored over 40 peer-reviewed
publications, which have amassed nearly 2000 citations. Dr. Wang has led or
contributed to multiple significant scientific research projects and has been
invited to give keynote presentations at over ten academic conferences.
---
Wenbo Wang received the master’s degree from North China Electric Power
University, Beijing, China, in 2020. She is currently a Ph.D. candidate with
the School of Cyber Security, University of Chinese Academy of Sciences,