边缘计算下差分隐私的应用研究综述

doi:10.11896/jsjkx.230700089

摘要/Abstract

摘要： 为了解决传统云计算模式的延迟和带宽限制,应对物联网和大数据时代的需求,边缘计算开始崭露头角并逐渐受到广泛关注。在边缘计算环境下,用户数据的隐私问题成为了一个重要的研究热点。差分隐私技术有着坚实的数学基础,它作为一种有效的隐私保护算法,已经被广泛应用于边缘计算中,两者的结合有效缓解了隐私保护低和计算成本高的问题。首先介绍了互联网发展带来的问题,其次介绍了边缘计算的基本概念、特点和组成部分,并概括了与传统云计算相比的优势,然后对差分隐私的基本概念和原理进行了概括,进而详细阐述了差分隐私的3种扰动方式和常用的实现机制,最后对边缘计算下差分隐私的应用研究进行了综述,并指出了未来的研究方向。总之,将差分隐私技术应用于边缘计算场景对隐私保护和数据分享都是一种有效保护手段。

关键词: 边缘计算, 差分隐私, 本地化差分隐私, 隐私保护, 实时数据处理

Abstract: In order to address the latency and bandwidth limitations of the traditional cloud computing model and to cope with the demands of the Internet of Things and the big data era,edge computing is making its appearance and gaining widespread attention.In the edge computing environment,the privacy of user data has become an important research hotspot.The combination of differential privacy techniques,which have a solid mathematical foundation,has been widely used in edge computing as an effective privacy-preserving algorithm to improve the problem of low privacy protection and high computational cost.The problems brought about by the development of the Internet are firstly introduced,followed by the basic concepts,features and components of edge computing,and the advantages compared with traditional cloud computing are outlined.The basic concepts and principles of differential privacy are again outlined,followed by a detailed description of the three perturbation methods and common implementation mechanisms of differential privacy,and finally the research on the application of differential privacy under edge computing is reviewed.Finally,the research on the application of differential privacy under edge computing is reviewed and future research directions are pointed out.In conclusion,the application of differential privacy techniques to edge computing scenarios is an effective means to protect privacy and data sharing.

Key words: Edge computing, Differential privacy, Local differential privacy, Privacy preserving, Real-time data processing

中图分类号:

TP309.2

孙剑明, 赵梦鑫. 边缘计算下差分隐私的应用研究综述[J]. 计算机科学, 2024, 51(6A): 230700089-9. https://doi.org/10.11896/jsjkx.230700089

SUN Jianming, ZHAO Mengxin. Survey of Application of Differential Privacy in Edge Computing[J]. Computer Science, 2024, 51(6A): 230700089-9. https://doi.org/10.11896/jsjkx.230700089

参考文献

[1]GE B,WU C,ZHANG T H,et al.Privacy Protection Method ofEdge Computing Based on Federated Learning[J].Journal of Anhui University of Science and Technology(Natural Science Edition),2022,42(6):79-86.
[2]LI X W,CHEN B H,YANG D Q,et al.Review of Security Protocols in Edge Computing Environments[J].Journal of Computer Research and Development,2022,59(4):765-780.
[3]MYKTAR Y,MOHD Y I I,AINUDDIN W B A W,et al.Systematic Review on Security and Privacy Requirements in Edge Computing:State of the Art and Future Research Opportunities[J].IEEE Access,2020,8:76541-76567.
[4]LIU X F,QU Y X,WANG Y,et al.Privacy intelligent inference prediction in Edge Computing environment[J].Artificial Intelligence,2019(5):45-54.
[5]SHEN C N.Research progress of edge computing security and privacy Protection[J].Network Security and Data Governance,202,41(8):41-48.
[6]DWORK C.Differential privacy:A survey of results[J].Foundations and Trends © in Theoretical Computer Science,2011,9(3/4):211-407.
[7]DWORK C,ROTHA.The Algorithmic foundations of differential privacy[J].Found. Trends Theor. Comput. Sci.,2014,9(3/4):211-407.
[8]ZHAO Y Q,YANG M.A review of Differential privacy Re-search[J].Journal of Computer Science,2023,50(4):265-276.
[9]XIONG P,ZHU T Q,WANG X F.Differential Privacy Protection and its Application[J].Chinese Journal of Computers,2014,37(1):101-122.
[10]YE Q Q,MENG X F,ZHU M J,et al.A review of localized differential privacy[J].Journal of Software,2018,29(7):1981-2005.
[11]YANG M M,LAM K Y,ZHU T Q,et al.SPoFC:A framework for stream data aggregation with local differential privacy[J].Concurrency and Computation:Practice and Experience,2022,35(5).https://doi.org/10.1002/cpe.7572.
[12]TAN Z W,ZHANG L F.Review of Machine learning privacyProtection[J].Journal of Software,2020,31(7):2127-2156.
[13]CHEN Q.Research on Privacy Protection of Private Location by Edge Computing Difference[D].Lanzhou:Lanzhou Jiaotong University,2020.
[14]ZHANG L,LIU Y,WANG R Z.Data publishing technologybased on differential privacy in Location Big Data Service[J].Journal of Communications,2016,37(9):46-54.
[15]YU N W,YANG S J,CHEN Z G,et al..LBS user location Privacy Protection scheme based on Differential privacy[J].Journal of Hebei University of Science and Technology,2021,42(3):222-230.
[16]ZHANG Q Y,ZHANG X,LI W J,et al.Overview of Location Trajectory Privacy Protection Technology Based on LBS System[J].Computer Application Research,2020,37(12):3534-3544.
[17]ZHANG X J,HE F C,GAI J Y,et al.Differential private fe-deral learning model for fingerprint indoor location under edge computing[J].Computer Research and Development,2022,59(12):2667-2688.
[18]HE F C.Research on key technologies of indoor location federated learning model supporting privacy protection in edge computing environment[D].Lanzhou:Lanzhou Jiaotong University,2022.
[19]ZHANG Q Y,ZHANG X,LI W J,et al.Design of Interest Point Recommendation Algorithm Based on Differential Privacy Protection[J].Computer Applications and Software,2019,36(9):243-248,269.
[20]ZHANG X J,YANG H Y,LI Z,et al.Differential Private Location Privacy Protection Method Integrating Semantic Positions[J].Computer Science,2021,48(8):300-308.
[21]JONG S K,JONG W K,YON D C.Successive Point-of-Interest Recommendation with Local Differential Privacy[J].arXiv:1908.09485,2019.
[22]AFZAL,KIRAN,TARIQ,et al.An Optimized and EfficientRouting Protocol Application for IoV[J].Mathematical Pro-blems in Engineering,2021,2021(Pt.23):9977252.1-9977252.32.
[23]DENG Y K,ZHANG L,LI J.Summary of privacy protection research on the Internet of Vehicles[J].Computer Application Research,2022,39(10):2891-2906.
[24]ZHAO P,ZHANG G L,WAN S H,et al.A survey of local differential privacy for securing internet of vehicles[J].The Journal of Supercomputing,2019,76(11):8391-8412.
[26]WU M Q,HUANG X M,KANG J W,et al.Research on Diffe-rential Privacy Protection for Vehicle Road Collaborative Infe-rence[J].Computer Engineering,2022,48(7):29-35.
[25]RAZA S,WANG S G,AHMED A,et al.A Survey on Vehicular Edge Computing:Architecture,Applications,Technical Issues,and Future Directions[J].Wireless Communications and Mobile Computing,2019,2019:3159762:1-3159762:19.
[27]ZHONG X Y,LI M H,LI L H.Summary of Research on Privacy Protection of Vehicle Internet Location[J].Internet of Things Technology,2023,13(3):77-79.
[28]XIE S S,LIU H L,ZHAO G S.Differential Privacy Protection Method Based on Location Semantics in the Internet of Vehicles[J].Small Micro Computer Systems,2023,45(4):984-990.
[29]XU C,DING Y Y,LUO L,et al.Personalized Location Privacy Protection Based on Location Services in the Internet of Vehicles[J].Journal of Software,2022,33(2):699-716.
[30]QU P R.The Application of Internet of Things Technology in Smart Healthcare[J].Internet Weekly,2022(22):45-47.
[31]AN J,NING H J.Research on Security and Privacy Protection for Smart Medical Networks[J].China New Communications,2022,24(17):122-124,134.
[32]AHAD A,ALI Z,MATEEN A,et al.A Comprehensive review on 5G-based Smart Healthcare Network Security:Taxonomy,Issues,Solutions and Future research directions[J/OL].Array,2023,18.https://doi.org/10.1016/j.array.2023.100290.
[33]LI B S.Privacy Protection System in Edge Intelligent Collaborative Computing Mode[J].Electronic Testing,2022,36(20):59-62.
[34]LIU X,ZHOU P,QIU T,et al.Blockchain-Enabled Contextual Online Learning under Local Differential Privacy for Coronary Heart Disease Diagnosis in Mobile Edge Computing[J].IEEE Journal of Biomedical and Health Informatics,2020,20(8):2177-2188.
[35]SUN C,LI H,LI X,et al.Convergence of Recommender Sys-tems and Edge Computing:A Comprehensive Survey[J].IEEE Access,2020,8:47118-47132.
[36]FENG H,YIN H W,LI X H,et al.Summary of privacy protection research in recommendation systems[J].Computer Science and Exploration,2023,17(8):1814-1832.
[37]ZHENG X Y,LUO Y L,WANG X S,et al.Research on Distri-buted Differential Privacy Recommendation Method Based on Location Services[J].Journal of Electronic Science,2021,49(1):99-110.
[38]DU Y J,ZHOU D Y,XIE Y,et al.Federated matrix factorization for privacy-preserving recommender systems[J/OL].Applied Soft Computing Journal,2021,111.https://doi.org/10.1016/j.asoc.2021.107700.
[39]DU M K,PENG J J,HU Y J,et al.A Logistic Regression Matrix Decomposition Recommendation Algorithm Satisfying Differential Privacy[J].Journal of Beijing University of Posts and Telecommunications,2023,46(3):115-120.
[40]GAI N,XUE K P,ZHU B,et al.An efficient data aggregation scheme with local differential privacy in smart grid[J].Digital Communications and Networks,2022,8(3):333-342.
[41]KHAN H M,KHAN A,KHAN B,et al.Fault-Tolerant Secure Data Aggregation Schemes in Smart Grids:Techniques,Design Challenges,and Future Trends[J].Energies,2022,15(24):9350.
[42]ALAGGAN M,GAMBS S,KERMARREC M A.Heterogeneous Differential Privacy[J].Journal of Privacy and Confidentiality,2017,7(2).
[43]YANG L.Research on Differential Privacy Protection Methods for Heterogeneous Information Networks[D].Harbin:Harbin Institute of Technology,2019.

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