Key Technologies and Research Prospects of Privacy Computing

Abstract

Abstract: Privacy computing, as an important technical means taking into account both data circulation and privacy protection, can effectively break the “data island” barriers while ensuring data security, it enables open data sharing, and promotes the deep mining and use of data and crossdomain integration. In this paper, the background knowledge, basic concepts and architecture of privacy computing were introduced, the basic concepts of three key technologies of privacy computing, including secure multiparty computation, federated learning and trusted execution environment were elaborated, and studies on the existing privacy security was conducted, a multidimensional comparison and summarization of the differences of the three key technologies were made. On this basis, the future research direction of privacy computing is prospected from the technical integration of privacy computing with blockchain, deep learning and knowledge graph.

Key words: privacy computing, secure multiparty computation, federated learning, trusted execution environment, blockchain

摘要： 隐私计算作为目前兼顾数据流通和隐私保护的重要技术手段，能在确保数据安全的同时有效打破“数据孤岛”壁垒，实现数据开放共享，促进数据的深度挖掘使用和跨领域融合.介绍隐私计算的背景知识、基本概念以及体系架构，分别阐述隐私计算3种关键技术安全多方计算、联邦学习和可信执行环境的基本概念，对其存在的隐私安全进行研究，并对3种关键技术的差异进行多维度的比较总结.在此基础上，以隐私计算与区块链、深度学习、知识图谱的技术融合为出发点对隐私计算的未来研究方向进行展望.

关键词: 隐私计算, 安全多方计算, 联邦学习, 可信执行环境, 区块链

沈传年, 徐彦婷, 陈滢霞. 隐私计算关键技术及研究展望[J]. 信息安全研究, 2023, 9(8): 714-.

References

［1］Li F H, Li H, Niu B, et al. Privacy computing: Concept, computing framwork, and future development trends［J］. Engineering, 2019, 5(6): 11791192, 13071322［2］隐私计算联盟, 中国信息通信研究院云计算与大数据研究所. 隐私计算白皮书［EBOL］. 2021 ［20230709］. https:www.doc88.comP39239696189777.html［3］Shamir A. How to share a secret［J］. Communications of the ACM, 1979, 22(11): 612613［4］Blakley G R. Safeguarding cryptographic keys［C］ Proc of Int Workshop on Managing Requirements Knowledge. Los Alamitos, CA: IEEE Computer Society, 1979: 313313［5］庞辽军, 姜正涛, 王育民. 基于一般访问结构的多重秘密共享方案［J］. 计算机研究与发展, 2006, 43(1): 3338［6］Harn L. Secure secret reconstruction and multisecret sharing schemes with unconditional security［J］. Security and Communication Networks, 2014, 7(3): 567573［7］张艳硕, 赵瀚森, 陈辉焱, 等. 概率型2选1不经意传输协议的方案设计［J］. 密码学报, 2021, 8(2): 282293［8］朱晓玲, 陆阳, 侯整风, 等. VANET中基于不经意传输和群签名的强隐私保护方案［J］. 计算机应用研究, 2014, 31(1): 226230［9］李晔. 具有隐私保护的外包数据分类方法研究［D］. 哈尔滨: 哈尔滨工业大学, 2018［10］Mezzour G, Perrig A, Gligor V, et al. Privacypreserving relationship path discovery in social networks［C］ Proc of the 8th Int Conf on Cryptology and Network Security (CANS 2009). Berlin: Springer, 2009: 189208［11］罗小双, 杨晓元, 王绪安. 一类可抵抗恶意攻击的隐私集合交集协议［J］. 计算机应用, 2017, 37(6): 15931598［12］Khoshgozaran A, Shahabi C, ShiraniMehr H. Location privacy: Going beyond Kanonymity, cloaking and anonymizers［J］. Knowledge and Information Systems, 2011, 26(3): 435465［13］陈杨杨, 黄征, 徐礼吏. 多服务器环境下的隐私信息检索［J］. 信息安全与通信保密, 2011, 9(12): 100102［14］Shokri R, Stronati M, Song C, et al. Membership inference attacks against machine learning models［C］ Proc of 2017 IEEE Symp on Security and Privacy (SP). Piscataway, NJ: IEEE, 2017: 318［15］Bonawitz K, Ivanov V, Kreuter B, et al. Practical secure aggregation for privacypreserving machine learning［C］ Proc of the 2017 ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2017: 11751191［16］杨波, 冯登国, 秦宇, 等. 基于TrustZone的可信移动终端云服务安全接入方案［J］. 软件学报, 2016, 27(6): 13661383［17］Zhang Y, Li Y, Fang L, et al. Privacyprotected electronic voting system based on blockchin and trusted execution environment［C］ Proc of the 5th IEEE Int Conf on Computer and Communications (ICCC). Piscataway, NJ: IEEE, 2019: 12521257［18］朱建明, 张沁楠, 高胜, 等. 基于区块链的隐私保护可信联邦学习模型［J］. 计算机学报, 2021, 44(12): 24642484［19］熊璐, 杨阳, 沙金锐, 等. 基于区块链的隐私保护交集算法［J］. 通信技术, 2020, 53(7): 17681773［20］祝烈煌, 高峰, 沈蒙, 等. 区块链隐私保护研究综述［J］. 计算机研究与发展, 2017, 54(10): 21702186［21］王慧, 王励成, 柏雪, 等. 区块链隐私保护和扩容关键技术研究［J］. 西安电子科技大学学报, 2020, 47(5): 2839［22］Rouhani B D, Riazi M S, Koushanfar F. Deepsecure: Scalable provablysecure deep learning［C］ Proc of the 55th Annual Design Automation Conf. 2018: 16［23］Makri E, Rotaru D, Smart N P, et al. EPIC: Efficient private image classification［C］ Proc of CTRSA. Berlin: Springer, 473492［24］王利娥, 李小聪, 刘红翼. 融合知识图谱和差分隐私的新闻推荐方法［J］. 计算机应用, 2022, 42(5): 13391346［25］陈名杨, 张文, 陈湘楠, 等. 群体知识图谱: 分布式知识迁移与联邦式图谱推理［J］. 智能科学与技术学报, 2022, 4(1): 5564

[1]	. Research on Malicious Location Attack Detection of VANET Based on Federated Learning [J]. Journal of Information Security Reserach, 2023, 9(8): 754-.
[2]	. Civil Aviation Passenger Privacy Data Protection Method Based on Multiparty Security Attack and Defense Game [J]. Journal of Information Security Reserach, 2023, 9(8): 799-.
[3]	. A Novel Blockchain Privacy Preserving Scheme Based on Paillier and FO Commitment [J]. Journal of Information Security Reserach, 2023, 9(4): 306-.
[4]	. Local Protection of Power Data Prediction Model Based on Federated Learning and Homomorphic Encryption [J]. Journal of Information Security Reserach, 2023, 9(3): 228-.
[5]	. Compliance Analysis Framework of Personal Information Protection in Federated Learning [J]. Journal of Information Security Reserach, 2023, 9(2): 162-.
[6]	. Research on 5G Edge Computing Security Based on the Trusted Execution Environment [J]. Journal of Information Security Reserach, 2023, 9(1): 38-.
[7]	. DCR Defense Mechanism of Federated Learning Model for Data Governance Poison [J]. Journal of Information Security Reserach, 2022, 8(4): 357-.
[8]	. A Survey on Threats to Federated Learning [J]. Journal of Information Security Reserach, 2022, 8(3): 223-.
[9]	. [J]. Journal of Information Security Reserach, 2022, 8(3): 270-.
[10]	. Research on Smart Contract Method in the Framework of Secure Multi-party Computation [J]. Journal of Information Security Reserach, 2022, 8(10): 956-.
[11]	. Efficient Distributed Anonymous Heterogeneous Cross-domain Authentication Scheme for Industrial Internet of Things [J]. Journal of Information Security Reserach, 2022, 8(10): 964-.
[12]	. A Privacy-Preserving Federated Learning Method for Traffic Flow Prediction [J]. Journal of Information Security Reserach, 2022, 8(10): 1035-.
[13]	. Research on Acceleration Method of Searchable Encryption of Private Data Based on Trusted Hardware [J]. Journal of Information Security Reserach, 2021, 7(4): 319-327.
[14]	. AI and Data Privacy Protection: The Way to Federated Learning [J]. Journal of Information Security Research, 2019, 5(11): 961-965.