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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 167-178页

作者： Qian Hu Siqi Bu Wencong Su Vladimir Terzija the Department of Physics Hong Kong Baptist UniversityHong KongChina the Department of Electrical and Electronic Engineering Shenzhen Research InstituteThe Hong Kong Polytechnic UniversityHong KongChina the Department of Electrical and Computer Engineering University of Michigan-DearbornDearbornMI 48128USA the School of Engineering Newcastle UniversityNewcastleUK

Active distribution network(ADN),as a typically cyber-physical system,develops with the evolution of Internet of Things(IoTs),which makes the network vulnerable to cybersecurity threats.In this paper,the eavesdropping attacks that lead to privacy breaches are addressed for the IoT-enabled ADN.A privacy-preserving energy management system(EMS)is proposed and empowered by secure data exchange protocols based on the homomorphic cryptosystem.During the information transmission among distributed generators and load customers in the EMS,private information including power usage and electricity bidding price can be effectively protected against eavesdropping attacks.The correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically guaranteed.The simulation results demonstrate the effectiveness and the computational efficiency of the proposed homomorphically encrypted EMS.

关键词： Eavesdropping attack energy management system homomorphic cryptosystem Internet of Things(IOTs) active distribution network(ADN) privacy-preserving

privacy-preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

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Computers, Materials & Continua 2024年第3期78卷 3177-3194页

作者： Bin Wu Xianyi Chen Jinzhou Huang Caicai Zhang Jing Wang Jing Yu Zhiqiang Zhao Zhuolin Mei School of Computer and Big Data Science Jiujiang UniversityJiujiang332005China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Engineering Hubei University of Arts and ScienceXiangyang441053China School of Modern Information Technology Zhejiang Institute of Mechanical and Electrical EngineeringHangzhou310053China Information Center Jiangxi Changjiang Chemical Co.Ltd.Jiujiang332005China School of Mathematics and Computer Science Ningxia Normal UniversityGuyuan756099China

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

关键词： privacy-preserving adjacency query multi-keyword fuzzy search encrypted graph

A Comprehensive Survey for privacy-preserving Biometrics: Recent Approaches, Challenges, and Future Directions

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Computers, Materials & Continua 2024年第2期78卷 2087-2110页

作者： Shahriar Md Arman Tao Yang Shahadat Shahed Alanoud AlMazroa Afraa Attiah Linda Mohaisen School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation techniques.Biometric credentials are increasingly being used for verification due to their advantages over traditional methods,making it crucial to safeguard the privacy of people’s biometric data in various scenarios.This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems.It proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing literature.We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities,such as face,fingerprint,and eye detection.The survey encompasses various categories of privacy-preserving mechanisms and examines the trade-offs between security,privacy,and recognition performance,as well as the issues and future research directions.It aims to provide researchers,professionals,and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies.

关键词： Biometric modalities biometric recognition biometric security privacy-preserving security threats

VPFL:A verifiable privacy-preserving federated learning scheme for edge computing systems

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数字通信与网络:英文版 2023年第4期9卷 981-989页

作者： Jiale Zhang Yue Liu Di Wu Shuai Lou Bing Chen Shui Yu School of Information Engineering Yangzhou UniversityYangzhou225009China Water Conservancy and Civil Engineering College Inner Mongolia Agricultural UniversityHohhot010018China Deakin Blockchain Innovation Lab School of Information TechnologyDeakin UniversityMelbourneVIC3125Australia College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing211106China School of Computer Science University of Technology SydneySydney2007Australia

Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based privacy-preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.

关键词： Federated learning Edge computing privacy-preserving Verifiable aggregation Homomorphic cryptosystem

A verifiable privacy-preserving data collection scheme supporting multi-party computation in fog-based smart grid

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Frontiers of Computer Science 2022年第1期16卷 143-153页

作者： Zhusen LIU Zhenfu CAO Xiaolei DONG Xiaopeng ZHAO Haiyong BAO Jiachen SHEN Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai200062China Cyberspace Security Research Center Peng Cheng LaboratoryShenzhen518055China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China

Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the grid.Recently,much attention has been paid to the research on smart grid,especially in protecting privacy and data aggregation.However,most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog computation.In this paper,we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC),named VPDC-MPC,to achieve both functions simultaneously in smart grid based on fog computing.VPDC-MPC realizes verifiable secret sharing of users’data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing scheme.Besides,we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’report.Furthermore,VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis(not only data aggregation)via secure multi-party computation between cloud servers in smart grid.Besides,VPDC-MPC tolerates fault of cloud servers and resists collusion.We also present security analysis and performance evaluation of our scheme,which indicates that even with tradeoff on computation and communication overhead,VPDC-MPC is practical with above features.

关键词： smart grid fog computing data aggregation verifiable secret sharing error detection secure multi-party computation secure function query privacy-preserving

DPPS: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services

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Frontiers of Computer Science 2023年第5期17卷 197-205页

作者： Long LI Jianbo HUANG Liang CHANG Jian WENG Jia CHEN Jingjing LI College of Cyber Security Jinan UniversityGuangzhou 510632China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Department of Computer Applications Guilin University of Technology at NanningNanning 530000China

Since smartphones embedded with positioning systems and digital maps are widely used,location-based services(LBSs)are rapidly growing in popularity and providing unprecedented convenience in people’s daily lives;however,they also cause great concern about privacy leakage.In particular,location queries can be used to infer users’sensitive private information,such as home addresses,places of work and appointment locations.Hence,many schemes providing query anonymity have been proposed,but they typically ignore the fact that an adversary can infer real locations from the correlations between consecutive locations in a continuous LBS.To address this challenge,a novel dual privacy-preserving scheme(DPPS)is proposed that includes two privacy protection mechanisms.First,to prevent privacy disclosure caused by correlations between locations,a correlation model is proposed based on a hidden Markov model(HMM)to simulate users’mobility and the adversary’s prediction probability.Second,to provide query probability anonymity of each single location,an advanced k-anonymity algorithm is proposed to construct cloaking regions,in which realistic and indistinguishable dummy locations are generated.To validate the effectiveness and efficiency of DPPS,theoretical analysis and experimental verification are further performed on a real-life dataset published by Microsoft,i.e.,GeoLife dataset.

关键词： location-based services privacy-preserving hidden Markov model k-anonymity query probability

A classification-based privacy-preserving decision-making for secure data sharing in Internet of Things assisted applications

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Digital Communications and Networks 2022年第4期8卷 436-445页

作者： Alaa Omran Almagrabi A.K.Bashir Department of Information Systems Faculty of Computing and Information Technology(FCIT)King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK

The introduction of the Internet of Things(IoT)paradigm serves as pervasive resource access and sharing platform for different real-time applications.Decentralized resource availability,access,and allocation provide a better quality of user experience regardless of the application type and scenario.However,privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data availability.In this paper,privacy-preserving decision-making for the data-sharing scheme is introduced.This scheme is responsible for improving the security in data sharing without the impact of replicated resources on communicating users.In this scheme,classification learning is used for identifying replicas and accessing granted resources independently.Based on the trust score of the available resources,this classification is recurrently performed to improve the reliability of information sharing.The user-level decisions for information sharing and access are made using the classification of the resources at the time of availability.This proposed scheme is verified using the metrics access delay,success ratio,computation complexity,and sharing loss.

关键词： Classification learning Data mining IoT privacy-preserving Resource replication

Blockchain-Enabled Secure and privacy-preserving Data Aggregation for Fog-Based ITS

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Computers, Materials & Continua 2023年第5期75卷 3781-3796页

作者： Siguang Chen Li Yang Yanhang Shi Qian Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing210003China School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing210003China

As an essential component of intelligent transportation systems(ITS),electric vehicles(EVs)can store massive amounts of electric power in their batteries and send power back to a charging station(CS)at peak hours to balance the power supply and generate profits.However,when the system collects the corresponding power data,several severe security and privacy issues are encountered.The identity and private injection data may be maliciously intercepted by network attackers and be tampered with to damage the services of ITS and smart grids.Existing approaches requiring high computational overhead render them unsuitable for the resource-constrained Internet of Things(IoT)environment.To address above problems,this paper proposes a blockchain-enabled secure and privacy-preserving data aggregation scheme for fog-based ITS.First,a fog computing and blockchain co-aware aggregation framework of power injection data is designed,which provides strong support for ITS to achieve secure and efficient power injection.Second,Paillier homomorphic encryption,the batch aggregation signature mechanism and a Bloom filter are effectively integrated with efficient aggregation of power injection data with security and privacy guarantees.In addition,the fine-grained homomorphic aggregation is designed for power injection data generated by all EVs,which provides solid data support for accurate power dispatching and supply management in ITS.Experiments show that the total computational cost is significantly reduced in the proposed scheme while providing security and privacy guarantees.The proposed scheme is more suitable for ITS with latency-sensitive applications and is also adapted to deploying devices with limited resources.

关键词： Blockchain fog computing security privacy-preserving ITS

OTFS-Based Efficient Handover Authentication Scheme with privacy-preserving for High Mobility Scenarios

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China Communications 2023年第1期20卷 36-49页

作者： Dawei Li Di Liu Yu Sun Jianwei Liu School of Cyber Science and Technology Beihang UniversityBeijing 100191China

Handover authentication in high mobility scenarios is characterized by frequent and shortterm parallel execution.Moreover,the penetration loss and Doppler frequency shift caused by high speed also lead to the deterioration of network link quality.Therefore,high mobility scenarios require handover schemes with less handover overhead.However,some existing schemes that meet this requirement cannot provide strong security guarantees,while some schemes that can provide strong security guarantees have large handover overheads.To solve this dilemma,we propose a privacy-preserving handover authentication scheme that can provide strong security guarantees with less computational cost.Based on Orthogonal Time Frequency Space(OTFS)link and Key Encapsulation Mechanism(KEM),we establish the shared key between protocol entities in the initial authentication phase,thereby reducing the overhead in the handover phase.Our proposed scheme can achieve mutual authentication and key agreement among the user equipment,relay node,and authentication server.We demonstrate that our proposed scheme can achieve user anonymity,unlinkability,perfect forward secrecy,and resistance to various attacks through security analysis including the Tamarin.The performance evaluation results show that our scheme has a small computational cost compared with other schemes and can also provide a strong guarantee of security properties.

关键词： high mobility condition handover authentication privacy-preserving Tamarin OTFS

PUMTD:privacy-preserving User-Profile Matching Protocol in Social Networks

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China Communications 2022年第6期19卷 77-90页

作者： Jianhong Zhang Haoting Han Hongwei Su Zhengtao Jiang Changgen Peng School of Information Sciences and Technology North China University of TechnologyBeijing 100043China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal UniversityHangzhou 311121China Guizhou Provincial Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China School of Computer and Cyber Sciences Communication University of ChinaBeijing 100025China

User profile matching can establish social relationships between different users in the social network.If the user profile is matched in plaintext,the user's privacy might face a security challenge.Although there exist some schemes realizing privacypreserving user profile matching,the resource-limited users or social service providers in these schemes need to take higher computational complexity to ensure the privacy or matching of the data.To overcome the problems,a novel privacy-preserving user profile matching protocol in social networks is proposed by using t-out-of n servers and the bloom filter technique,in which the computational complexity of a user is reduced by applying the Chinese Remainder Theorem,the matching users can be found with the help of any t matching servers,and the privacy of the user profile is not compromised.Furthermore,if at most t-1 servers are allowed to collude,our scheme can still fulfill user profile privacy and user query privacy.Finally,the performance of the proposed scheme is compared with the other two schemes,and the results show that our scheme is superior to them.

关键词： user profile matching Chinese remainder theorem privacy-preserving query privacy