Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.

Differential Privacy Techniques for Cyber Physical Systems: A Survey

We are living in a world where massive end devices perform computing everywhere and everyday. However, these devices are constrained by the battery and computational resources. With the increasing number of intelligent applications (e.g., augmented reality and face recognition) that require much more computational power, they shift to perform computation offloading to the cloud, known as mobile cloud computing (MCC). Unfortunately, the cloud is usually far away from end devices, leading to a high latency as well as the bad quality of experience (QoE) for latency-sensitive applications. In this context, the emergence of edge computing is no coincidence. Edge computing extends the cloud to the edge of the network, close to end users, bringing ultra-low latency and high bandwidth. Consequently, there is a trend of computation offloading toward edge computing. In this paper, we provide a comprehensive perspective on this trend. First, we give an insight into the architecture refactoring in edge computing. Based on that insight, this paper reviews the state-of-the-art research on computation offloading in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing. Then, we illustrate some disruptive application scenarios that we envision as critical drivers for the flourish of edge computing, such as real-time video analytics, smart “things” (e.g., smart city and smart home), vehicle applications, and cloud gaming. Finally, we discuss the opportunities and future research directions.

Computation Offloading Toward Edge Computing

Metaverse, as an evolving paradigm of the next-generation Internet, aims to build a fully immersive, hyper spatiotemporal, and self-sustaining virtual shared space for humans to play, work, and socialize. Driven by recent advances in emerging technologies such as extended reality, artificial intelligence, and blockchain, metaverse is stepping from science fiction to an upcoming reality. However, severe privacy invasions and security breaches (inherited from underlying technologies or emerged in the new digital ecology) of metaverse can impede its wide deployment. At the same time, a series of fundamental challenges (e.g., scalability and interoperability) can arise in metaverse security provisioning owing to the intrinsic characteristics of metaverse, such as immersive realism, hyper spatiotemporality, sustainability, and heterogeneity. In this paper, we present a comprehensive survey of the fundamentals, security, and privacy of metaverse. Specifically, we first investigate a novel distributed metaverse architecture and its key characteristics with ternary-world interactions. Then, we discuss the security and privacy threats, present the critical challenges of metaverse systems, and review the state-of-the-art countermeasures. Finally, we draw open research directions for building future metaverse systems.

A Survey on Metaverse: Fundamentals, Security, and Privacy

The ever-growing demand for electrical energy of sensing devices in the Internet of Things (IoT) has led to generating large amounts of electricity consumption data. Electricity service providers often use wireless sensor networks to collect sensing devices’ electricity consumption data for statistical analysis, so as to provide sensing devices with improved electrical services. As an important data mining technique, while data clustering excels in dealing with such massive data, it imposes the risk of privacy disclosure in the process of data clustering. In an effort of solving this problem, Blum et al. proposed a differential privacy $ {k}$ -means algorithm, effectively preventing privacy disclosure. However, the availability of data clustering results is reduced due to the data distortion in Blum’s algorithm. In this paper, we propose a privacy and availability data clustering (PADC) scheme based on $ {k}$ -means algorithm and differential privacy, which enhances the selection of the initial center points and the distance calculation method from other points to center point. Moreover, PADC attempts to reduce the outlier effect through detecting outliers during the clustering process. Security analysis indicates that our scheme satisfies the goal of differential privacy and prevents privacy information disclosure. Meanwhile, performance evaluation shows that our scheme, at the same privacy level, improves the availability of clustering results compared to the existing differential privacy $ {k}$ -means algorithms, suggesting that our proposed PADC scheme outperforms others for intelligent electrical service in IoT.

Enhancing Privacy and Availability for Data Clustering in Intelligent Electrical Service of IoT

With the rapid digitalization of various industries, mobile crowdsensing (MCS), an intelligent data collection and processing paradigm of the industrial Internet of Things, has provided a promising opportunity to construct powerful industrial systems and provide industrial services. The existing unified privacy strategy for all sensing data results in excessive or insufficient protection and low quality of crowdsensing services (QoCS) in MCS. To tackle this issue, in this article we propose a personalized privacy protection (PERIO) framework based on game theory and data encryption. Initially, we design a personalized privacy measurement algorithm to calculate users’ privacy level, which is then combined with game theory to construct a rational uploading strategy. Furthermore, we propose a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness. Theoretical analysis and ample simulations with real trajectory dataset indicate that the PERIO scheme is effective and makes a reasonable balance between retaining high QoCS and privacy.

A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT

K-means algorithm is an important type of clustering algorithm and the foundation of some data mining methods. But it has the risk of privacy disclosure in the process of clustering. In order to solve this problem, Blum et al. proposed a differential privacy K-means algorithm, which can prevent privacy disclosure effectively. However, the availability of clustering results is reduced due to the added noise. In this paper, we propose a novel DPLK-means algorithm based on differential privacy, which improves the selection of the initial center points through performing the differential privacy K-means algorithm to each subset divided by the original dataset. Performance evaluation shows that our algorithm improves the availability of clustering results compared to the existing differential privacy K-means algorithm at the same privacy level.

DPLK-Means: A Novel Differential Privacy K-Means Mechanism

In cloud computing environment, especially in big data era, adversary may use data deduplication service supported by the cloud service provider as a side channel to eavesdrop users' privacy or sensitive information. In order to tackle this serious issue, in this paper, we propose a secure data deduplication scheme based on differential privacy. The highlights of the proposed scheme lie in constructing a hybrid cloud framework, using convergent encryption algorithm to encrypt original files, and introducing differential privacy mechanism to resist against the side channel attack. Performance evaluation shows that our scheme is able to effectively save network bandwidth and disk storage space during the processes of data deduplication. Meanwhile, security analysis indicates that our scheme can resist against the side channel attack and related files attack, and prevent the disclosure of privacy information.

A Secure Data Deduplication Scheme Based on Differential Privacy

Support vector machine (SVM) as a learning machine has shown a good learning ability and generalization ability in classification, regression and forecasting. Because of its excellent learning performance, SVM has always been a hotspot in machine learning. In hence, this paper will make a more systematic introduction of SVM, including the theory of SVM, the summarization and comparison of the quadratic programming optimizations and parameter optimizations for SVM and the introduction of some new SVMs, like FSVM, TSVM, MSVM, etc. Then, the applications of SVM in real life will be presented, especially, in the area of mobile multimedia. Finally, we conclude with a discussion of the direction of further SVM improvement.

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Research Survey on Support Vector Machine

The explosive growth of multimedia data promotes us to enter the era of big data. To improve the storage efficiency and reduce the management expenditure of these massive data, deduplication is a promising technology to meet these requirements. However, it arises serious privacy concerns and poses new security challenges, such as privacy leakage and unauthorized access. To tackle these problems, in this paper, we propose a novel role symmetric encryption (RSE) algorithm and construct an authorized deduplication scheme (RSEDup) based on the role symmetric encryption to achieve the authorized deduplication in cloud. The RSEDup scheme is the first solution to prevent privacy leakage and achieve the authorized deduplication effectively. Performance evaluation shows the efficiency of the proposed scheme.

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Jun Ren

Papers

Enhancing Privacy and Availability for Data Clustering in Intelligent Electrical Service of IoT

DPLK-Means: A Novel Differential Privacy K-Means Mechanism

A Secure Data Deduplication Scheme Based on Differential Privacy

Research Survey on Support Vector Machine

A novel role symmetric encryption algorithm for authorized deduplication in cloud