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

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 *** owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud *** on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure *** issue of privacy data protection has become an important obstacle to data sharing and *** to query outsourcing graph data safely and effectively has become the focus of *** 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 *** 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 *** 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 *** proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental *** 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

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A privacy-preserving Energy Management System Based on Homomorphic Cryptosystem for IoT-enabled Active Distribution Network

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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 *** 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 *** 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 *** correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically *** 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

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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 *** 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 *** paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric *** proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing *** 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 *** 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 *** 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

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PARE:privacy-preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

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Computers, Materials & Continua 2024年第8期80卷 3067-3084页

作者： Peicong He Yang Xin Yixian Yang School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China

The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data ***,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters may be concerned about the validity of the collected ***,it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing(SC)data collection tasks with *** this end,this paper proposes a privacy-preserving data reliability evaluation for SC in IoT,named ***,we design a data uploading format using blockchain and Paillier homomorphic cryptosystem,providing unchangeable and traceable data while overcoming privacy ***,based on the uploaded data,we propose a method to determine the approximate correct value region without knowing the exact ***,we offer a data filtering mechanism based on the Paillier cryptosystem using this value *** evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.

关键词： Spatial crowdsourcing privacy-preserving data evaluation IoT blockchain

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privacy-preserving Data Publishing for Multiple Numerical Sensitive Attributes

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Tsinghua Science and Technology 2015年第3期20卷 246-254页

作者： Qinghai Liu Hong Shen Yingpeng Sang School of Computer and Information Technology Beijing Jiaotong University School of Information Science and Technology Sun Yat-sen University School of Computer Science University of Adelaide

Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitive- attributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication, in this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization （MSB） to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.

关键词： privacy-preserving k-anonymity numerical sensitive attribute clustering Multi-Sensitive Bucketization(MSB)

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privacy-preserving large-scale systems of linear equations in outsourcing storage and computation

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Science China(Information Sciences) 2018年第3期61卷 148-156页

作者： Dongmei LI Xiaolei DONG Zhenfu CAO Haijiang WANG Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Lab of Trustworthy Computing East China Normal University

Along with the prevalence of cloud computing, it can be realised to efficiently outsource costly storage or computations to cloud servers. Recently, secure outsourcing mechanism has received more and more attention. We focus on secure outsourcing storage and computation for large-scale systems of linear equations(LEs) in this paper. Firstly, we construct a new efficient matrix encryption scheme. Then we exploit this encryption scheme to develop a new algorithm which can implement outsourcing storage and computation for large-scale linear equations in the semi-honest setting. Compared with the previous work,the proposed algorithm requires lower storage overhead and is with competitive efficiency.

关键词： cloud computing privacy-preserving linear equations encryption security

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privacy-preserving Recommendation Based on Kernel Method in Cloud Computing

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Computers, Materials & Continua 2021年第1期66卷 779-791页

作者： Tao Li Qi Qian Yongjun Ren Yongzhen Ren Jinyue Xia College of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China College of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China College of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) NYUSA

The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively promoted the intelligent development of these *** the IoT has gradually grown in recent years,there are still many problems that need to be overcome in terms of technology,management,cost,policy,and *** need to constantly weigh the benefits of trusting IoT products and the risk of leaking private *** avoid the leakage and loss of various user data,this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization,which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized *** to non-negative matrix factorization privacy-preserving algorithm,the new algorithm does not need to know the detailed information of the data,only need to know the connection between each data;and the new algorithm can process the data points with negative *** show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users’personal privacy.

关键词： IoT kernel method privacy-preserving personalized recommendation random perturbation

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privacy-preserving Decision Protocols Based on Quantum Oblivious Key Distribution

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Computers, Materials & Continua 2020年第9期64卷 1915-1928页

作者： Kejia Zhang Chunguang Ma Zhiwei Sun Xue Zhang Baomin Zhou Yukun Wang School of Computer Science and Technology Harbin Engineering UniversityHarbin150001China School of Mathematical Science Heilongjiang UniversityHarbin150080China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China Center for Quantum Computing Peng Cheng LaboratoryShenzhen518055China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Shenzhen PolytechnicShenzhen518055China Department of Electrical and Computer Engineering National University of Singapore117583Singapore

Oblivious key transfer(OKT)is a fundamental problem in the field of secure multi-party *** makes the provider send a secret key sequence to the user obliviously,i.e.,the user may only get almost one bit key in the sequence which is unknown to the ***,a number of works have sought to establish the corresponding quantum oblivious key transfer model and rename it as quantum oblivious key distribution(QOKD)from the well-known expression of quantum key distribution(QKD).In this paper,a new QOKD model is firstly proposed for the provider and user with limited quantum capabilities,where both of them just perform computational basis measurement for single *** we show that the privacy for both of them can be protected,since the probability of getting other’s raw-key bits without being detected is exponentially ***,we give the solutions to some special decision problems such as set-member decision and point-inclusion by announcing the improved shifting strategies followed ***,the further discussions and applications of our ideas have been presented.

关键词： Quantum cryptography quantum computing privacy-preserving quantum oblivious key distribution set-member decision point-inclusion decision

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privacy-preserving Deep Learning on Big Data in Cloud

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China Communications 2023年第11期20卷 176-186页

作者： Yongkai Fan Wanyu Zhang Jianrong Bai Xia Lei Kuanching Li State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing 100024China Industrial and Commercial Bank of China Zhuhai Branch Zhuhai 519000China Dept.of Computer Science and Information Engineering Providence UniversityTaichung 43301China

In the analysis of big data,deep learn-ing is a crucial *** data analysis tasks are typically carried out on the cloud since it offers strong computer capabilities and storage ***-ertheless,there is a contradiction between the open nature of the cloud and the demand that data own-ers maintain their *** use cloud resources for privacy-preserving data training,a viable method must be found.A privacy-preserving deep learning model(PPDLM)is suggested in this research to ad-dress this preserving *** preserve data privacy,we first encrypted the data using homomorphic en-cryption(HE)***,the deep learn-ing algorithm’s activation function—the sigmoid func-tion—uses the least-squares method to process non-addition and non-multiplication operations that are not allowed by ***,experimental re-sults show that PPDLM has a significant effect on the protection of data privacy *** with Non-privacy preserving Deep Learning Model(NPPDLM),PPDLM has higher computational effi-ciency.

关键词： big data cloud computing deep learning homomorphic encryption privacy-preserving

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privacy-preserving deep learning techniques for wearable sensor-based big data applications

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Virtual Reality & Intelligent Hardware 2022年第3期4卷 210-222页

作者： Rafik HAMZA Minh-Son DAO Integrated Big Data Research Center|NICT-National Institute of Information and Communications Technology TokyoJapan

Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)*** technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's *** the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security *** this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable *** highlight the fundamental features of security and privacy for wearable device ***,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable *** also present a case study on privacy-preserving machine learning ***,we theoretically and empirically evaluate the privacy-preserving deep learning framework's *** explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption ***,we explore the obstacles and gaps in the deployment of practical real-world *** a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.

关键词： Wearable technology Augmented reality privacy-preserving Deep learning Big data Secure prediction service

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