# A Study on the Predictability of Sample Learning Consistency

Alain Raymond-Sáez  Julio Hurtado  Álvaro Soto

###### Abstract

Curriculum Learning is a powerful training method that allows for faster and
better training in some settings. This method, however, requires having a
notion of which examples are difficult and which are easy, which is not always
trivial to provide. A recent metric called C-Score acts as a proxy for example
difficulty by relating it to learning consistency. Unfortunately, this method
is quite compute intensive which limits its applicability for alternative
datasets. In this work, we train models through different methods to predict
C-Score for CIFAR-100 and CIFAR-10. We find, however, that these models
generalize poorly both within the same distribution as well as out of
distribution. This suggests that C-Score is not defined by the individual
characteristics of each sample but rather by other factors. We hypothesize
that a sample’s relation to its neighbours, in particular, how many of them
share the same labels, can help in explaining C-Scores. We plan to explore
this in future work.

Machine Learning, ICML

##  1 Introduction

While Deep Learning has achieved significant milestones over the last decade,
training strategies have remained relatively unchanged. Over the last years,
strides have been made in improving these strategies. One such approach is
Curriculum Learning (CL) (Bengio et al., 2009; Wu et al., 2021). CL strategies
are based on providing a model with an increasingly difficult selection of
examples, similar to how children learn in school. CL strategies are popular
in Reinforcement Learning -where a curriculum of tasks is progressively
learned - and Natural Language Processing applications - where commonly
sentence length has been a proxy for difficulty.

The problem, however, lies in how to determine the difficulty of samples.
Typically, an ad hoc difficulty measure needs to be crafted by hand for a
specific problem, which limits their application in practice. Even worse, for
some applications like image classification, sometimes there’s no natural
difficulty ordering that can be created. This has led to the development of
more general measures of difficulty: Self Paced Learning(Kumar et al., 2010)
utilizes the current loss of an example as a proxy for difficulty; however,
this requires extra computation and is prone to overfitting(Jiang et al.,
2015). As an alternative, a metric called C-Score was proposed (Jiang et al.,
2021). This metric measures how consistently a particular example is learned
across various models, that is, it measures the proportion of iterations where
a sample is successfully classified. The logic behind this metric is supported
by the empirical observation that samples are generally learned in the same
order (Toneva et al., 2019) -independent of the model’s architecture- and that
a significant number of examples are learned once and never forgotten. The
drawback to this metric is that it requires inordinate amounts of computing
for a single dataset, as it requires several models to be trained and averaged
over, negating many of the practical benefits of applying CL in the first
place.

A reasonable strategy to alleviate the problems of computing C-Scores would be
to learn a model that could extrapolate C-Scores to unseen datasets. This
could be done by training a model on datasets where the score is already
calculated. In order for this strategy to be successful, C-Scores should be
mainly determined by each sample’s features. However, we know C-Scores must
necessarily depend also on the labels associated to the dataset, as it has
been seen(Arpit et al., 2017) that natural images with random labels are
harder to learn. This leads us to ask the following research questions:

  * •

How much does C-Score depend on a sample’s features? How much does it depend
on the task?

  * •

Is there enough information in a sample’s features to predict reasonable
estimates of C-Score for similar unseen tasks?

  * •

If so, are these estimates capable of creating useful curricula for unseen
tasks?

To answer these questions, we attempt to learn models that predict C-Score
through a variety of different methods. We find, however, that all methods
produce models that generalize poorly even in the same dataset, while they
barely generalize above random chance to unseen datasets.

Our current findings suggest that C-Score cannot be explained solely by a
sample’s features, even when training multiple models on this task. Our
current hypothesis, which we plan to explore in future work, is that
understanding how a sample relates to its neighbours, in particular, whether
those neighbours belong to the same class or not can shed some light into
explaining C-Scores, as well.

##  2 Methodology

(a) CIFAR-10

(b) CIFAR-100

Figure 1: C-Score Distribution for datasets used in our experiments.

C-Score provides a measure of how consistently a sample is learned by a model.
In particular, it defines the average ratio of iterations for which a group of
models correctly predict the label of a particular sample. Therefore, it is
defined in the range [0,1], with higher values indicating easier to learn
examples.

We attempt to predict C-Scores by three different methods: regression,
Bayesian Personalized Ranking (BPR)(Rendle et al., 2012) and classification by
binning C-Scores. These methods were selected as they provide different
relaxations to the original problem with regression being the hardest and
binning the simplest version of the same problem.

###  2.1 Experiment Setup

We use ResNet18(He et al., 2016) as our architecture of choice. Each
experiment is run for 200 epochs using a learning rate of 0.001, with 1 epoch
warmup. We use learning rate scheduling with a decay rate of $0.2$ at 60, 120,
160 epochs. We use weight decay of $5e4$. All experiments use batch size 256.
We performed experiments with both pretrained models and randomly initialized
models. All results correspond to randomly initialized models as they
performed equal or better than pretrained models. All experiments are run
using 10 different seeds.

For Regression and BPR, we change the last classification layer for a linear
layer with one output. Adding a sigmoid activation function after this layer
made no difference in performance, so we skip them in reported experiments.

###  2.2 Datasets

We work with CIFAR-10(Krizhevsky et al., 2009a) and CIFAR-100(Krizhevsky et
al., 2009b) as these are the datasets for which there are published C-Scores.
As C-Scores are only defined for the training sets of these datasets, we
partition them into training and test sets by a random 80/20 split. As shown
in Figure 1, CIFAR-10 shows a skewed distribution of C-Scores, with most
scores being around 0.8, while CIFAR-100 and ImageNet show a more uniform
distribution of scores, with peaks near 0 and 1.

###  2.3 Regression

We will start by trying to learn models that directly regress C-Score based on
an input image. We use Mean Squared Error (MSE) as our loss function.

###  2.4 Bayesian Personalized Ranking

BPR was originally conceived as a method to learn how to rank items in
Recommender Systems, where it is useful to generate sequences of
recommendations and learning how to do it from actual user preferences. This
translates perfectly to our need to learn how to produce difficulty orderings
that relate to the ground truth C-Scores that we know. To achieve this, BPR
defines a loss function that encourages the model to learn how to rank a pair
of samples appropriately. The intuition behind this method is that instead of
directly learning how to regress a score, we learn how to score such that for
any given pair of examples the ordering is correct. In particular, given a
pair of samples $x_{1},x_{2}$, and an ordering function $O(x)$ where
$O(x_{2})>O(x_{1})$ the BPR loss is traditionally defined as:

$BPR(x_{1},x_{2})=-log(\sigma(x_{2}-x_{1}))$

During our experiments, we found this function to diverge and subsequently
removed the logarithm. Thus our modified BPR loss is:

$BPR_{m}(x_{1},x_{2})=\sigma(x_{1}-x_{2})$

In order to train with this loss, we simply forward propagate the sampled
batch, we produce every possible pair between samples from that batch,
calculate the BPR loss for each pair and backpropagate. With a batch size of
256, this amounts to 32.640 pairs per batch.

###  2.5 Binning

As direct regression might prove too difficult a task to learn, we attempt to
train a model on a relaxed version of the problem. First, we divide the
C-Score range of [0,1] in equal width bins. Then, we train a model to learn to
classify which bin a certain image belongs to given a traditional Cross
Entropy Loss. We run experiments with 5, 10, 20, 40 bins. Given the
distribution of C-Scores, we tried training models weighting losses to take
into account these imbalances. Results were no better than regular training,
thus we will not report on those results.

###  2.6 Evaluation

All models are trained on CIFAR-100 and evaluated on two test sets: the
CIFAR-100 test set and CIFAR-10. CIFAR-100 is meant to test in distribution
generalization, while CIFAR-10 is meant to test out of distribution
generalization.

In the case of BPR, as it is not computationally feasible to generate every
possible test pair, we do an approximation by doing a fixed permutation of the
test set and evaluating all pairs of the retrieved batches (of size 512).

Finally, we evaluate all models via Spearman Rank Correlation (SRC) to
understand the quality of the orderings produced by each model. When using
Binning, the ground truth ordering is defined by the bin to which each sample
belongs.

Figure 2: Test MSE for a Regression Model trained on CIFAR-100 C-Scores. In
distribution performance remains stable, while out of distribution
generalization degrades over time.

(a) Test Performance on CIFAR-100

(b) Out of Distribution Generalization on CIFAR-10

Figure 3: Generalization performance increase over performance of a random
baseline using Bins of size {5,10,20,40} training with CIFAR-100 C-Scores. As
can be seen, performance within the same task achieves poor generalization.
Out of distribution generalization barely surpasses random guessing. Figure 4:
Generalization performance increase over performance of a random baseline
using BPR training with CIFAR-100 C-Scores. A Random Baseline should achieve
50% accuracy. As can be seen, performance within the same task achieves poor
generalization. Out of distribution generalization barely surpasses random
guessing. This suggests that image features on their own, do little to explain
sample learning consistency.

##  3 Results

As can be seen in Figure 2, direct regression of the C-Score seems to work
poorly with MSEs around 0.1. This implies an average error of around 0.3 when
generalizing to the test set of CIFAR-100. Matters become worse when
evaluating generalization to CIFAR-10, with MSE values reaching 0.16 which
imply an average error of 0.3. This is significant when predicting scores in
the C-Score range [0,1], suggesting poor prediction performance. Binning
results are shown in Figure 3. The same pattern holds: generalization in
CIFAR-100 is far greater than in CIFAR-10. CIFAR-10 performance is barely
above random chance for all number of bins. This might be due to CIFAR-10’s
C-Scores skew towards values around 0.8. Increasing the number of bins rapidly
degrades performance (as expected of a harder task). This decrease, however,
is quite extreme showing that models learn hypotheses that do not generalize
well. For BPR (Figure 4), we observe the same behaviour, with CIFAR-10 and
CIFAR-100 performance degrading to 4% and 14% above random chance.

For all methods, generalization capabilities within the same dataset are quite
low, while extrapolation to a new task is barely above random chance, which
suggests that the task may require additional information to be solved. One
could argue, however, that an exact prediction is not exactly what is needed,
just a reasonable ordering. Thus while we may have poor models at predicting
exact scores, they may in the end produce decent orderings. To test this, we
calculate the Spearman Rank Correlation (SRC) between predictions of all our
models against the ground truth. Results are shown in Table 1. Correlations
within the same dataset move between 0.28 and 0.44, with BPR being the best
performing method of them all. This is a reasonable amount of correlation, but
hardly explains the C-Score ordering. This is most evident when we look at
results when testing on CIFAR-10. All methods drastically reduce their
performance, with Binning even producing negative correlations in some cases.

Thus, we are left to conclude that image features by themselves are not the
main determining factor in learning consistency of samples. This is expected
as the task itself should also determine the difficulty of learning a sample:
it has been seen that natural images with random labels are learned more
slowly(Arpit et al., 2017). What is not expected is how little they seem to
generalize to out of distribution samples, even when producing simplified
versions of the problem such as when using Binning and BPR. Future work should
focus on finding other factors that explain C-Score. In particular, we believe
that considering how similar are the labels for nearby samples can help
explain difficulty. (Jiang et al., 2021) explore this and find that such
metrics only work well after training. They do not attempt to do this with
ImageNet pretrained models, which we think can give valuable information.

Table 1: Spearman Rank Correlation (SRC) for different models trained on
CIFAR-100 on test sets of CIFAR-10 (out of distribution) and CIFAR-100 (in
distribution). Average over 10 different seeds.
Dataset | Method | SRC | Std. Dev
---|---|---|---
CIFAR-100 | BPR | 0.443082 | 0.006912
CIFAR-100 | REGRESSION | 0.368591 | 0.011231
CIFAR-100 | BINS-5 | 0.300274 | 0.009779
CIFAR-100 | BINS-10 | 0.326956 | 0.008409
CIFAR-100 | BINS-20 | 0.337483 | 0.009155
CIFAR-100 | BINS-40 | 0.324599 | 0.008783
CIFAR-10 | BPR | 0.227766 | 0.022339
CIFAR-10 | REGRESSION | 0.202101 | 0.014833
CIFAR-10 | BINS-5 | 0.102341 | 0.009429
CIFAR-10 | BINS-10 | 0.105720 | 0.006573
CIFAR-10 | BINS-20 | 0.132004 | 0.011930
CIFAR-10 | BINS-40 | 0.137987 | 0.010076

##  4 Related Work

Methods for ranking the difficulty of examples are standard in the Curriculum
Learning literature. Traditional Curriculum Learning(Bengio et al., 2009) uses
a fixed ordering that remains unchanged during training. Self Paced
Learning(Kumar et al., 2010) methods derive difficulty relative to the current
hypothesis of the model, usually based on a function of the model’s loss. For
the image domain, work has been done to define difficulty in terms of the time
needed for humans to search for objects in images(Ionescu et al., 2016).
Recent methods have focused on finding difficulty metrics that are based on
what is empirically difficult for deep learning models. Metrics such as how
often examples are forgotten(Toneva et al., 2019), learning consistency(Jiang
et al., 2021) and prediction depth (Baldock et al., 2021) have been proposed
to enable Curriculum Learning for arbitrary datasets. (Jiang et al., 2021)
also study distance-based and learning-speed based proxies to their C-Score
method, however, both still require training on the dataset to achieve
competitive results, unlike the current work which seeks to find methods to
predict C-Scores on unseen datasets.

##  5 Conclusions

Three methods for predicting C-Score were tested: Regression, Binning and BPR.
BPR proved the more successful method with the greatest Spearman Rank
Correlation. All methods, however, were poor at predicting C-Scores on the
same dataset as well as on a new dataset. This suggests that C-Scores are not
predictable from image features alone. Our hypothesis is that C-Scores also
depend on the task, in particular, in the relation between a sample and its
neighbours labels. We will attempt to verify this in future work. We also plan
on including results using Inception(Szegedy et al., 2015) and models trained
on ImageNet(Deng et al., 2009), as features learned from CIFAR-100 may be too
simple to transfer learn to a different dataset.

##  6 Acknowledgements

We would like to thank Carlos Aspillaga for early inspiration for this paper.
This research has been financially supported by the National Center for
Artificial Intelligence CENIA FB210017, Basal ANID.

## References

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##  Appendix A Contribution of LatinX Individuals

All authors identify as LatinX. Alain Raymond-Sáez came up with the idea,
developed code, experiments, analysis and wrote the paper. Julio Hurtado
contributed with code, experiment suggestions and discussion. Álvaro Soto
oversaw the whole research, with suggestions and discussion.


# Security and Privacy for Mobile Edge Caching: Challenges and Solutions

Jianbing Ni, Kuan Zhang, Athanasios V. Vasilakos  J. Ni is with the Department
of Electrical and Computer Engineering, Queen’s University, Kingston, Ontario,
Canada K7L 3N6. Email<EMAIL_ADDRESS>
K. Zhang is with Department of Electrical and Computer Engineering, University
of Nebraska-Lincoln, Omaha, NE 68182 USA. Email<EMAIL_ADDRESS>V.
Vasilakos is with the School of Electrical and Data Engineering, University
Technology Sydney, Australia, and with the Department of Computer Science,
Electrical and Space Engineering, Lulea University of Technology, Lulea,
97187, Sweden<EMAIL_ADDRESS>

###### Abstract

Mobile edge caching is a promising technology for the next-generation mobile
networks to effectively offer service environment and cloud-storage
capabilities at the edge of networks. By exploiting the storage and computing
resources at the network edge, mobile edge caching can significantly reduce
service latency, decrease network load, and improve user experience. On the
other hand, edge caching is subject to a number of threats regarding privacy
violation and security breach. In this article, we first introduce the
architecture of mobile edge caching, and address the key problems regarding
why, where, what, and how to cache. Then, we examine the potential cyber
threats, including cache poisoning attacks, cache pollution attacks, cache
side-channel attacks, and cache deception attacks, which result in huge
concerns about privacy, security, and trust in content placement, content
delivery, and content usage for mobile users, respectively. After that, we
propose a service-oriented and location-based efficient key distribution
protocol (SOLEK) as an example in response to efficient and secure content
delivery in mobile edge caching. Finally, we discuss the potential techniques
for privacy-preserving content placement, efficient and secure content
delivery, and trustful content usage, which are expected to draw more
attention and efforts into secure edge caching.

###### Index Terms:

Edge Caching, Multi-access Edge Computing, Security and Privacy, Content
Placement, Content Sharing.

##  I Introduction

Due to the vastly increased number of connected devices in the Internet of
Things (IoT) [1], wireless networks are expected to provide supremely high
data rates and extremely low latency to enable the emerging applications, such
as remote control of smart vehicles, industrial automation, and
virtual/augmented reality. By integrating the disruptive technologies, mobile
devices have ultra-reliable and affordable network access to satisfy the
quality of experience in wireless environments, and thereby making our life
more convenient. As one of the key technologies, multi-access edge computing
[2] is becoming increasingly popular that offers service environment and
cloud-computing capabilities in an effective manner at the edge of wireless
networks. By leveraging the computing and storage resources on the edge nodes
(e.g., macro base stations, Wi-Fi access points, network switches, video
cameras, and roadside infrastructure), popular contents can be temporarily
maintained on the edge nodes to respond to frequent access requests from user
equipment, which is referred to as mobile edge caching [3]. Since the contents
are delivered directly from edge nodes instead of from the remote cloud if
they are cached on edge nodes when requested, network edge caching can
significantly reduce the content delivery latency, alleviate the backhaul
traffic, and improve the spectrum efficiency and the energy efficiency [4].

Despite the appealing benefits of the quality of service, caching at the
network edge has been subject to a number of controversies regarding security
breach and privacy violation. For example, Denial-of-Service (DoS) attacks and
wireless jamming can consume the bandwidth and computing resources at the
network edge. The open edge network creates the real-world attack surface for
adversaries that aim to compromise or control the edge caches. The attackers
might acquire the stored data at the edge caches that are located at their
vicinity, as well as the data that are from other locations in distributed
edge storage, such that the sensitive information can be extracted because of
the context awareness. In addition, the edge network providers might be
curious about the contents stored on their caches and utilize the inference
attacks to identify the hidden information about mobile users, such as
trajectory and personal identifiable information. However, with limited
computation, power, and memory, complex secure and privacy-preserving
mechanisms cannot be straightforwardly deployed on the edge caches. Therefore,
it is of paramount importance to explore deeper understanding of the potential
attacks and design practical real-world solutions for reasoning about the
security and privacy of mobile edge caching.

In this article, we aim to examine security and privacy threats in mobile edge
caching, and explore new models, techniques, and approaches to reason about
the security, privacy, and trust of mobile edge caching. Specifically, we
start with the architecture of mobile edge caching and address the key issues
about edge caching, i.e., why to cache, where to cache, what to cache, and how
to cache. We then examine a series of threats on security and privacy that are
grouped into two categories: 1) generic threats, such as DoS attacks, wireless
jamming, malware attacks, and man-in-the-middle attacks; and 2) cache-specific
threats, including cache poisoning attacks, cache pollution attacks, cache
side-channel attacks, and cache deception attacks. However, the research about
edge caching security is still in the early stage. We discuss the privacy,
security, and trust challenges in mobile edge caching, and explore the
potential solutions to preserve privacy in content placement, security in
content delivery, and trust in content usage. To identify the approaches of
securing content delivery, we propose a Service-Oriented and Location-based
Efficient Key distribution protocol (SOLEK) for secure content sharing based
on the certificateless proxy re-encryption as an example. Finally, we discuss
future research directions of privacy-preserving content placement, efficient
and secure content delivery, and trustful content usage, to draw more efforts
for securing edge caching.

##  II Overview of Network Edge Caching

In this section, we present the general architecture of mobile edge caching.
As shown in Fig. 1, mobile edge caching involves several entities, i.e., user
equipment, access networks, mobile core network, and the Internet. User
equipment (UE), such as smart phones, smart vehicles, smart meters, and smart
home produces, carried by mobile users or deployed in ground, are capable of
accessing different services in the Internet through access networks and
mobile core networks. In access networks, different wireless communication
technologies, including radio access network, Wi-Fi, small cells, and 5G new
radio, are leveraged to build communication channels for UE with the support
of infrastructure, such as base stations, edge routers, and switches. Mobile
core network is the heart and acts as an anchor point for multi-access
technologies. 5G core comprises of pure, virtualized, software-based network
functions or services, and can be instantiated within multi-access edge-cloud
computing infrastructures. The Internet is the data network that provides a
large variety of services to the connecting mobile users.

Figure 1: Mobile Edge Caching.

The problems of why, where, what, and how to cache are the key issues in
mobile edge caching. Why to cache refers to the advantages of mobile edge
caching regarding the quality of experience of mobile users. Where to cache
refers to location selection for caching contents. What to cache refers to the
caching contents that have short lifetimes and consists of different types,
such as documents, videos, radios, and IoT data, and how to cache refers to
caching policy selection and caching scheme design.

Why to cache? The primary reason for caching is to make data access less
expensive on time, bandwidth, and resource costs.

  * •

Service Delay Reduction: By storing content close to mobile users, the service
delay can be significantly reduced. This feature is quite attractive for data
delivery of latency-sensitive applications, such as video streaming that will
account for 78% of all mobile traffic by 2021.

  * •

Traffic Burden Releasing: As duplicate contents cause severe traffic burden
over hackhaul links, the traffic can be reduced up to 35% by storing the
requested contents at the network edge [2]. Caching in 3G and 4G LTE networks
can reduce mobile traffic up to two thirds [5].

  * •

Energy and Spectral Efficiency Improvement: Energy and spectral efficiency can
be improved by caching contents at user devices, or by caching at base
stations or edge routers to eliminate the backhaul bottleneck. Spectral
efficiency and energy efficiency can benefit from wireless edge about 900% and
500%, respectively [4].

Where to cache? As depicted in Fig. 1, the caches can be placed on the devices
at the network edge, including edge in wired network and edge in wireless
network.

  * •

Caching at UE: Caching at UE is referred to device-to-device caching. The
devices are organized into clusters and directly communicate with each other
using license-band or unlicense-band protocols. The requested content can be
offered by some other UE within the same cluster that are caching the content.

  * •

Caching at Base Stations (BSs): Caching at BSs is referred to deploy caching
at micro base stations (MBSs), small base stations (SBSs), pico base stations
(PBS), and femto base stations (FBSs). Generally, MBSs have larger cache
spaces and coverage areas than SBSs, and SBSs use cooperative caching to share
contents with each other. PBSs and FBSs are special SBSs. In 5G era, C-RAN is
an important architecture of 5G cellular network [6]. C-RAN aggregates the
computing capabilities to baseband unit (BBU) pool, and deploys multiple radio
remote heads (RRHs) to address network capacity and coverage issues. Cache
spaces are deployed at both BBU and RRH that form a distributed edge caching
architecture. Besides, at wireless network edge, caching can be located at
wireless routers for enabling various applications, e.g., smart home and smart
office.

In addition, the contents can be cached at wired network edge, i.e., on edge
routers.

What to cache? The aim of caching is to reduce backhaul traffic and service
delay, so that the network providers prefer to cache all the contents that may
be requested by users. Nevertheless, the storage spaces of edge caches are
limited and the scale of contents is growing significantly [4]. Thus, it is
important to determine what content to be cached by considering popularity. In
fact, only a small number of content is extremely popular, while a long tail
of content is accessed by a small portion of mobile users. “Cacheability” is
changing over caching strategies and content types [5]. For example, it is
unnecessary to cache the contents that have been maintained on the neighboring
caches, and some contents with special types, e.g., images and videos, may
have the highest priority to be cached as they have the highest revisit rate.
Moreover, only public and static content, such as images, CSS files, PDF
documents, and videos will be cached. The cached contents are not user-
specific.

How to cache? According to when to decide whether to cache contents on edge
nodes, caching can be classified as reactive caching and proactive caching.
Reactive caching determines whether to cache a particular content after it has
been requested, while proactive caching determines what contents to be cached
before they are requested. Both reactive caching and proactive caching make
the decision based on the content types (e.g., files, software packages, and
videos), content popularity (i.e., the ratio of the number of requests in a
particular region), or the profiles of potential interested users (e.g.,
access patterns and mobility patterns). The content popularity and the
profiles of interested users may vary with time. The cached contents would be
updated based on the caching policies or strategies. In general, the proactive
caching and reactive caching include the following steps:

  * •

Content Placement/Update: The objectives of content placement and content
update are consistent, i.e., to determine locations of contents for maximizing
cache-hit rate. An effective caching policy can accurately estimate the gains
for caching content by evaluating current and future popularity and optimizing
storage spaces and cache node selection. Therefore, in content placement and
content update, one of the key issues is to estimate the popularity of
content. The user mobility, social associations, and preferences are essential
criterions to predict content’s popularity, and enable effective content
placement and cache update to achieve high cache-hit rate.

  * •

Content Delivery: The UE requests the interested contents from the service
servers. After receiving content requests, edge nodes determine whether the
requested content is cached or not by finding the unique identifier for every
object, called cache key, in the cache. If a cache-hit happens, the edge node
directly delivers the requested content to UE; otherwise, it fetches the
content from the remote servers through the mobile core network.

  * •

Content Usage: The UE receives the requested contents from the edge nodes or
the remote servers. The UE needs to check that the contents are latest and
they are from the correct service providers. The UE not only makes use of the
obtained contents to enjoy the corresponding services, but also serves as the
cache node to offer contents to other UE within the cluster.

##  III Privacy, Security, and Trust

In this section, we introduce the potential cyber threats that corrupt
security, privacy, and trust in mobile edge caching, and discuss the issues
caused by these threats.

###  III-A Threats in Mobile Edge Caching

With the development of ubiquitous networking, the connected devices are
exposed to various cyber threats, ranging from generic threats, such as DoS
attacks, wireless jamming, malware attacks, and man-in-the-middle attacks, to
cache-specific threats, including cache poisoning attacks, cache pollution
attacks, cache side-channel attacks, and cache deception attacks. The generic
threats to edge networks are the same as the risks to the well-known
communication and information systems. For example, the DoS attacks flood edge
nodes with superfluous requests in an attempt to make the services offered by
the edge nodes unavailable to its intended UE. We omit the introduction of
generic attacks and refer to [7] for the details. The cache-specific attacks
are introduced as follows.

Cache poisoning attacks: An adversary inserts the forged or false contents
into the edge caches to deceive the users who request them. For example, by
utilizing the vulnerability of cache keys, an adversary creates a cache key
collision and manipulates the cache keys to cause a harmful or crafted
response to the request from the targeted UE. The attacker may request a
content with a query containing a malformed identifier, which may cause the
remote server get crash. As a consequence, the false content is returned and
cached on the edge node and served other mobile users who request it. The
cache poisoning attack could result in an attacker delivering false or error
HTML pages, javascript files, stylesheets, images, and videos to the mobile
users instead of legitimate contents. Therefore, the cache poisoning attack
threatens the availability and the correctness of network resources. The users
may be successively deceived until the poisoned cache is updated. The success
of this attack relies on the exploitable vulnerabilities on the edge caches.

Cache pollution attacks: An attacker pollutes caching balance by sending large
numbers of fabricated contents to the cache and thereby tricking the cache
into caching non-popular contents. Under this attack, the cache-hit rate of
the legitimate users is degraded, and their response time is increased. There
are two types of cache pollution attacks, i.e., location-disruption attacks
and false-locality attacks. In the location-disruption attack, an adversary
frequently requests some non-popular contents and squeezes out the popular
contents in caches. In the false-locality attack, an adversary continually
requests a set of non-popular contents and inserts them into the cache.

Cache side-channel attacks: By observing and measuring the activities of edge
caches, such as response time, power consumption, cache access, and returned
fault, an adversary can learn private information about mobile users and
cached contents. The cache side-channel attacks can be divided into different
types based on the knowledge that the adversaries have. For example, in cache-
timing attacks, by comparing the response time, an adversary can determine
whether particular content has been cached. In content deduplication attacks,
a copy-on-write content fault reveals the fact that the requested content is
deduplicated and users must have requested the identical content. In cache
privacy attacks, an adversary can learn the access history and other possible
private information of a particular user, if they share the same edge cache.

Cache deception attacks: The attacker firstly tricks the target user into
making a request to the private content that it is not permitted to access.
The attacker then submits the same request, and edge caches can return the
requested content containing the previously private data. For example, an
attacker crafts a URL request that points to the bank account information of a
victim, but appends to it a non-exist path of a static image, and tricks the
victim into making this request. The server will ignore the invalid suffix and
return the account details, and the cache node will maintain the content which
is identified as a static image. The attacker can make the same request to
retrieve the account details. Thus, the private data of the victim will be
disclosed.

The cache poisoning attacks and the cache pollution attacks may corrupt the
integrity of cached content, render the unavailability of requested data, and
degrade the trust of mobile users on the edge caching services. The cache
side-channel attacks and the cache deception attacks compromise the
confidentiality of contents on caches and invade the privacy of mobile users.
These threats on security, privacy, and trust may compromise all the appealing
benefits of mobile edge caching. Security, privacy, and trust are different
requirements for the reliability of mobile edge caching, but they are all
interrelated. If security or privacy are not protected in content placement or
content delivery, the trust in data usage cannot be built. Therefore, it is
essential to preserve security, privacy, and trust in the entire procedure of
mobile edge caching services.

###  III-B Security, Privacy, and Trust Challenges

We examine the typical issues in the whole procedure, from content placement
to content delivery and content usage.

Privacy in Content Placement: Compared to the increasing data volumes in
mobile networks, the storage space on edge nodes is always limited. It is
impossible to cache all data locally. Hence, identifying the optimal set of
contents that maximizes the utility of each cache becomes crucial. A common
approach of content set identification for placing contents is based on
content popularity. To estimate popularity, a machine-learning model [8, 9] is
needed to analyze historical access records, social association, and mobility
patterns of mobile users. However, machine learning is a double-edged sword.
While enabling the discovery of knowledge from a large volume of data, it may
invade user privacy by exposing potentially sensitive information. Such
privacy invasion may trigger serious results. For example, the exposure of
mobile users’ preferences may bring various unwelcome advertisements and
promotions. Therefore, questions that need to be addressed include, how to
achieve privacy-preserving content placement based on content popularity and
user preferences? How to predict content popularity using machine learning
without exposing the privacy of mobile users? It is essential to explore
privacy-preserving techniques and design advanced solutions as a long-term
vision for content placement in mobile edge caching.

Security in Content Delivery: UE searches and retrieves contents from the
correct caches if they are on edge nodes; otherwise, it has to request the
data from remote servers. Knowing what content is stored on the edge nodes at
any given moment is essential, while maintaining this knowledge is challenging
in a distributed cache network. The Bloom filter and its variants are common
approaches for space-efficient approximate membership representation, a useful
technique to indicate whether an element is a member of a large group. A
shortcoming of such approaches is the false positive rate, i.e., a given item
is indicated to be cached but actually not there. Othello hashing is used to
design a memory-efficient and fast localization method of edge nodes [10] to
reduce the rate of false locating, but Othello construction and update
operations are expensive. Besides, end-to-end encryption is the major method
of securing data transmission on public channels (e.g., the transport layer
security (TLS) protocol), while protecting contents maintained on edge nodes
against the cache side-channel attacks and the cache deception attacks, but it
seemingly signifies the death of edge caching. The contents cached on edge
nodes are encrypted for the first requesting mobile user, such that the
subsequent UE cannot decrypt the fetched data from the edge nodes. CryptoCache
[11], a secure content transmission protocol was designed based on symmetric
encryption to enable caching of encrypted contents on an untrusted edge node,
but the cloud server is still needed even if the requested contents are
cached. Content partitioning and scrambling [12] is an efficient solution to
achieve content confidentiality but cannot provide sufficient security
guarantees, i.e., semantic security; and attribute-based encryption [13]
offers high data security, but brings heavy computational overhead. Therefore,
questions include, how to design effective content locators on edge nodes for
optimal content exploration? How to design efficient data encryption schemes
for secure content delivery without involving remote cloud servers? Exploring
new models, techniques, and approaches to guarantee effective content
exploration and reason about the data confidentiality during content delivery
against the cache side-channel attacks and the cache deception attacks is
deserved to focus on.

Trust in Content Usage: Mobile users recover their contents of interest after
receiving the encrypted data from edge caches. Due to the security threats,
mobile users may have huge concerns regarding the trustworthiness of their
obtained contents, e.g., whether they have been corrupted by ill-intentioned
edge nodes, poisoned by adversaries with cache poisoning attacks, or are
unavailable when requested due to the cache pollution attacks. The trust
decline due to the violation of authenticity, integrity, and availability is
one of the major obstacles for the widespread of mobile edge caching. The
sophisticated techniques (e.g., content checking, message authentication, and
digital signature) may not be practically feasible to address these issues as
a whole. For example, the digital signature can guarantee the authenticity and
integrity of the content, but it cannot effectively prevent or detect cache
poisoning attacks, since in the cache poisoning attack, the response is from
the correct server, but the content is not the one requested. Moreover, they
bring extra computational and communication overheads to both edge nodes and
mobile users. These additional burdens may eventually ruin the major advantage
of edge caching, i.e., low-latency service response. Without efficient
integrity verification, the trustworthiness of cached contents depends on the
honesty of edge nodes, but trust evaluation in a distributed edge network is
not straightforward. Blockchain can potentially be leveraged to achieve
distributed trust management on behalf of a decentralized, transparent and
public ledger, but this research area remains a series of inherent issues,
e.g., privacy leakage and storage overload. Potential questions include, how
to design efficient content verification schemes to detect corrupted content,
and how to build a trustful edge-caching framework based on the blockchain and
smart contracts. These problems, if not properly resolved, will impede the
success of mobile edge caching.

##  IV SOLEK

We propose SOLEK, a service-oriented and location-based efficient key
distribution protocol based on certificateless proxy re-encryption [14] for
secure content delivery in mobile edge caching. SOLEK enables a mobile user to
decrypt the ciphertexts of the contents cached on the edge node. The
decryption capability is delegated based on the service type $ID_{S}$ and the
position of the edge node $ID_{i}$, such as GPS data. Specifically, a mobile
user is distributed with two separate secret-public key pairs. One is related
to the service type $ID_{S}$ that is given in the service registration, and
the other is about the edge node $ID_{i}$ that is allocated when entering the
coverage area of $ID_{i}$. Also, the mobile user can access the contents
cached by a neighboring edge node $ID_{j}$ of $ID_{i}$ by utilizing the proxy
re-encryption technique. The model of SOLEK is shown in Fig. 2, and the
detailed algorithms in SOLEK are described in Fig. 3.

Figure 2: Model of SOLEK Figure 3: SOLEK Algorithms

In SOLEK, a trusted authority (TA) first executes the Setup algorithm to
bootstrap the whole protocol. The TA can be the key generation center (KGC) in
the identity-based cryptosystem. With the input of the security parameter
$\kappa$, the Setup algorithm outputs the public parameter
$param=(G,p,P,P_{pub},H_{1},H_{2},H_{3},H_{4},H_{5})$ and the master secret
key $s$. Then, with the inputs $(param,s,ID_{S})$, TA runs the ServKeyExt
algorithm to generate the service secret key $y$ and the service public key
$SPK=(X,Y,R)$. $SPK$ is public and $y$ is sent to the mobile users who
register the service $ID_{S}$ through secure channels. When receiving $y$, the
mobile users can verify whether $y$ is correct by checking
$RP\overset{?}{=}Y+H_{1}(X,Y,ID_{S})P_{pub}$. Also, with the inputs
$(param,s,ID_{i})$, TA executes the EdgeKeyExt algorithm to generate the edge
secret key $a_{i}$ and the edge public key $EPK=(A_{i},B_{i},D_{i})$ for the
edge node $ID_{i}$. $EPK$ is public and $a_{i}$ is sent to the mobile user who
enters the coverage area of $ID_{i}$ through a secure channel. When receiving
$a_{i}$, the mobile user can verify whether $a_{i}$ is correct by checking
$D_{i}P\overset{?}{=}B_{i}+H_{1}(A_{i},B_{i},ID_{i},t_{i})P_{pub}$. Here,
$t_{i}$ is the current time slot, and the edge secret-public key pair can be
updated by TA periodically. If a mobile user $U$ requests the data $m$ of the
service $ID_{S}$ in the coverage area of $ID_{i}$, $U$ sends $ID_{S}$ and the
file name $m_{id}$ to $ID_{i}$. $ID_{i}$ checks whether $m$ is cached or not.
If not, i.e., cache miss, the request is forwarded to the server. The server
chooses a session key $k$ to encrypt $m$ to generate $E_{m}$ using a block
cipher, e.g., AES, and runs the Encrypt algorithm to encrypt $k$ to obtain
$(E_{1},E_{2})$. $(E_{1},E_{2},E_{m})$ is returned to $U$ and also cached on
$ID_{i}$. $U$ runs the Decrypt algorithm to decrypt $(E_{1},E_{2})$ and obtain
$k$ for data decryption. If yes, it means cache hit, that is,
$(E_{1},E_{2},E_{m})$ is cached on $ID_{i}$, $U$ can directly fetch
$(E_{1},E_{2},E_{m})$ from $ID_{i}$. Subsequently, if $m$ is requested by
anther mobile user $U^{\prime}$ in the coverage area of $ID_{j}$, a
neighboring edge node of $ID_{i}$, $ID_{i}$ runs the ReKeyGen algorithm to
generate the re-encryption key $RK=(\alpha_{ij},W_{ij},R_{ij})$, and the
ReEncrypt algorithm to transform $(E_{1},E_{2},E_{m})$ to
$(E_{1},E^{\prime}_{1},E_{2},W_{ij},R_{ij},E_{m})$ for $U^{\prime}$. Finally,
$U^{\prime}$ fetches $(E_{1},E^{\prime}_{1},E_{2},W_{ij},R_{ij},E_{m})$ from
$ID_{i}$, and runs the ReDecrypt algorithm to decrypt
$(E_{1},E^{\prime}_{1},E_{2})$ and obtain $k$ for data decryption.

In SOLEK, two properties are provided, i.e., content confidentiality and
content sharing. To ensure the content confidentiality in SOLEK, two types of
attackers should be considered. The one has registered the service $ID_{S}$
but is not in the coverage area of $ID_{i}$; the other is in the coverage area
but does not register the service $ID_{S}$. If neither can compromise SOLEK,
other attackers, e.g., non-registered $ID_{S}$ and outside $ID_{i}$ attackers,
cannot break SOLEK. SOLEK has two levels of ciphertexts, $(E_{1},E_{2},E_{m})$
and $(E_{1},E^{\prime}_{1},E_{2},W_{ij},R_{ij},E_{m})$. We ensure the
confidentiality of cached content, i.e., each type of attackers cannot gain
any knowledge from any level of ciphertexts. In order to formalize plaintext
leakage from ciphertexts, the distinguishability has been defined that an
attacker cannot identify the plaintext choice given an encryption of a
plaintext randomly chosen from a two-element plaintext space determined by the
attacker with probability significantly better than that of random guessing.
According to this property, the security of SOLEK can be reduced to the hard
assumption, i.e., the Decisional Diffie-Hellman (DDH) assumption.

SOLEK achieves efficient content sharing among mobile users in the same area
for the service of common interest. It provides higher security guarantees
than the scheme in [12], under the assumption that all the registered mobile
users will not proactively expose their interested contents. Also, SOLEK
avoids interactions with the server to retrieve the session key if cache hit
compared with CryptoCache [11]. Attribute-based encryption (ABE) provides
tremendous potential for fine-grained data sharing based on multiple
attributes, while SOLEK surpasses the ABE-based data sharing schemes, such as
PCWX [13] and PWXL[15], in terms of computational efficiency. The time costs
of content encryption and decryption in PCWX [13], PWXL[15], and SOLEK are
demonstrated in Fig. 4. Since the cached content is shared based on the
service type and edge location, two attributes are considered in PCWX [13] and
PWXL[15]. The experiments are conducted on a smartphone of HUAWEI MT2-L01 with
Kirin 910 CPU and 1.25MB memory. The Barreto-Naehrig curve is utilized and the
security parameter $\kappa=128$. Fig. 4(a) and Fig 4(b) demonstrate that SOLEK
is much more efficient than PCWX [13] and PWXL[15] on data encryption and
decryption, and Fig 4(c) illustrates the communication overhead of SOLEK in
content transmission is less than that of PCWX [13] and PWXL[15].

(a) Time Cost for Server on Encryption

(b) Time Cost for UE on Decryption

(c) Communication Overhead

Figure 4: Performance Comparison

##  V Future Directions

In this section, we further explore the potential techniques to address the
security, privacy, and trust challenges in mobile edge caching, which are
expected to attract attentions in the future research.

###  V-A Privacy-preserving Content Placement

Federated learning [9] can be utilized to predict content popularity. In
federated learning, the model parameters are locally trained on edge nodes,
and periodically exchanged and updated through unreliable network channels
between the edge nodes and the remote server. The private information of
mobile users does not need to be sent to the server. However, the information
is still vulnerable to being disclosed to edge nodes during historical data
collection. Central differential privacy (DP), multi-party computation, and
homomorphic encryption are possible to protect the confidentiality of content,
but each has inherent weaknesses. The central DP relies on a trusted
“aggregator” that holds the sensitive data of all individuals for protecting
their privacy, multi-party computation requires extra interactions between
local edge nodes, and homomorphic encryption causes heavy computational
overhead for UE. The privacy leakage may be possible to be prevented by
utilizing local differential privacy (LDP), in which individuals randomly
perturb their inputs to offer plausible deniability of their data without a
trusted party. However, this technique is still in its infancy, although a few
of approaches have been proposed to achieve strong privacy preservation in
federated learning that support shallow models such as logistic regression.
These approaches suffer from the intrinsic limitations that they either cause
poor estimation accuracy or only focus on simple datasets or tasks. Moreover,
they incur a significant privacy issue of local model, as they expose all
internal model states to white-box inference attacks. The major challenge of
LDP is that it introduces large noise to local training in federated learning,
such that the cache-hit rate is quite low. Therefore, it is essential to
design new data pre-processing mechanisms (e.g., data encoding, and matrix
permutation) or integrate distribution estimation techniques for privacy-
preserving federated learning in content placement.

###  V-B Efficient and Secure Content Delivery

The Cuckoo filter is an efficient data structure for membership check to
locate the requested contents on edge nodes for mobile users. Compared with
the Bloom filter, the Cuckoo filter supports the removal of unpopular content
and achieves constant lookup cost for edge nodes. However, false positive
matches are still possible. Thus, the naming policies of contents, including
flat naming and hierarchical naming, should be extended to avoid false
positives. Furthermore, in distributed edge caching, it is challenging to
locate the content in the neighbouring edge nodes and determine the time-to-
live (TTL) value of a content request. To ensure efficient content
localization, an edge node should maintain all Cuckoo filters of the edge
nodes within the TTL range that consume the linear storage space of the edge
node with the number of reachable edge nodes. To reduce storage costs, the
approaches to compressing the elements at the same indices of the Cuckoo
filters and encapsulating the content indices for the edge node are deserved
to investigate. In addition, the efficient connection checking mechanisms are
required to enable an edge node to check connections with its neighbouring
edge nodes periodically and update TTL values.

###  V-C Trustful Content Usage

The content is cached on an edge node once a mobile user requests, the
corresponding signature of the server can be maintained on the public
blockchain for authentication and integrity verification. Subsequently, mobile
users can check the content integrity based on the signature on the blockchain
when needed. Thus, it is unnecessary to forward the signature to the mobile
users. Also, secure binding of cache keys and contents (i.e., key-data
integrity) should be achieved against potential replace attacks and poisoning
attacks. The zero-knowledge proof can be used to ensure that the contents and
the cache keys on the blockchain are identical to those cached on the edge
nodes. This proof of consistency is essential to ensure content reliability
and integrity. Moreover, the behaviors of fog nodes should be regulated. Smart
contracts are designed to enable edge nodes to make commitments and enforce
punishment on untrusted or rogue edge nodes in case any misbehavior is
detected. However, the usage of smart contracts can only guarantee
enforcement, not privacy. That is, the privacy of mobile users will be
disclosed. Although the variants of zero-knowledge proofs, such as Zero-
Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and Zero-
Knowledge Succinct Transparent Argument of Knowledge (zk-STARKs) can offer
privacy-preserving payments and interactions, the open problems, including the
efficiency and the trusted setup in zk-SNARKs and proof size reduction in zk-
STARKs are receiving a lot of attentions in research.

##  VI Conclusion

In this article, we have studied the security, privacy, and trust issues in
mobile edge caching. In specific, we have answered four key questions in
mobile edge caching to have the better understanding, and introduced its
security threats towards the content in caches. The challenges of privacy,
security, and trust in content placement, content delivery, and content usage
of network edge caching are presented, respectively. In response to efficient
and secure content delivery, a service-oriented and location-based efficient
key distribution protocol (SOLEK) is proposed based on certificateless proxy
re-encryption. Finally, several interesting research issues and potential
techniques have been identified to shed light on the further research about
secure, privacy-preserving, and trustful mobile edge caching.

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|  Jianbing Ni (M’18) is currently an assistant professor with the Department
of Electrical and Computer Engineering, Queen’s University, Kingston, Canada.
He received his Ph.D. degree in Electrical and Computer Engineering from
University of Waterloo, Waterloo, Canada, in 2018. His research interests are
applied cryptography and network security, with current focus on edge
computing, mobile crowdsensing, Internet of Things, and Blockchain technology.
---|---
|  Kuan Zhang (S’13-M’17) received the Ph.D. degree in Electrical and Computer
Engineering from the University of Waterloo, Canada, in 2016. He was also a
postdoctoral fellow with Department of Electrical and Computer Engineering,
University of Waterloo, Canada. Since 2017, he has been an assistant professor
at the Department of Electrical and Computer Engineering, University of
Nebraska-Lincoln, USA. His research interests include security and privacy for
mobile social networks, e-healthcare systems, cloud computing and cyber
physical systems.
---|---
|  Athanasios V. Vasilakos is with the School of Electrical and Data
Engineering, University Technology Sydney, Australia, and with the Department
of Computer Science, Electrical and Space Engineering, Lulea University of
Technology, Lulea, 97187, Sweden. He served or is serving as an Editor for
many technical journals, such as the IEEE Transactions on Network and Service
Management, IEEE Transactions on Cloud Computing, IEEE Transactions on
Information Forensics and Security, IEEE Transactions on Cybernetics, IEEE
Transactions on NanoBioscience, IEEE Transactions on Information Technology in
Biomedicine, ACM Transactions on Autonomous and Adaptive Systems, and IEEE
Journal on Selected Areas in Communications. He is Web of Science
2017,2018,2019,2020 Highly Cited Researcher.
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