通信学报 ›› 2021, Vol. 42 ›› Issue (6): 195-212.doi: 10.11959/j.issn.1000-436x.2021120

• 综述 • 上一篇    

面向隐私保护的非聚合式数据共享综述

李尤慧子1, 殷昱煜1, 高洪皓2,3, 金一4, 王新珩5   

  1. 1 杭州电子科技大学计算机学院,浙江 杭州 310018
    2 上海大学计算机工程与科学学院,上海 200444
    3 韩国嘉泉大学计算机工程系,城南市 461701
    4 北京交通大学计算机与信息技术学院,北京 100044
    5 西交利物浦大学电气与电子工程系,江苏 苏州 215123
  • 修回日期:2021-03-10 出版日期:2021-06-01 发布日期:2021-06-01
  • 作者简介:李尤慧子(1989− ),女,河南新蔡人,博士,杭州电子科技大学副教授,主要研究方向为边缘计算、隐私安全、移动互联网计算、高能效系统
    殷昱煜(1980− ),男,重庆人,博士,杭州电子科技大学教授,主要研究方向为边缘计算、服务计算、大数据分析、软件形式化方法等
    高洪皓(1985− ),男,浙江临海人,博士,上海大学副教授、韩国嘉泉大学教授,主要研究方向为软件形式化验证、服务协同计算、无线网络和工业物联网、智能医学影像处理等
    金一(1982− ),女,河北石家庄人,博士,北京交通大学教授、博士生导师,主要研究方向为机器学习与认知计算、人工智能及应用、图像感知与识别
    王新珩(1968− ),男,山东平度人,博士,西交利物浦智能工程学院教授、博士生导师,主要研究方向为物联网、室内定位、智能化服务和智慧城市
  • 基金资助:
    国家重点研发计划基金资助项目(2020YFB2103805);国家自然科学基金资助项目(61802093);国家自然科学基金资助项目(61972358)

Survey on privacy protection in non-aggregated data sharing

Youhuizi LI1, Yuyu YIN1, Honghao GAO2,3, Yi JIN4, Xinheng WANG5   

  1. 1 School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
    2 School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    3 Department of Computer Engineering, Gachon University, Seongnam 461701, South Korea
    4 School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
    5 Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
  • Revised:2021-03-10 Online:2021-06-01 Published:2021-06-01
  • Supported by:
    The National Key Research and Development Program of China(2020YFB2103805);The National Natural Science Foundation of China(61802093);The National Natural Science Foundation of China(61972358)

摘要:

海量数据价值虽高但与用户隐私关联也十分密切,以高效安全地共享多方数据且避免隐私泄露为目标,介绍了非聚合式数据共享领域的研究发展。首先,简述安全多方计算及其相关技术,包括同态加密、不经意传输、秘密共享等;其次,分析联邦学习架构,从源数据节点和通信传输优化方面探讨现有研究;最后,整理对比面向隐私保护的非聚合式数据共享框架,为后续研究方案构建和运行提供支撑。此外,总结提出非聚合式数据共享领域的挑战和潜在的研究方向,如复杂多参与方场景、优化开销平衡、相关安全隐患等。

关键词: 隐私保护, 数据共享, 联邦学习, 安全多方计算

Abstract:

Although there is a great value hidden in the massive data, it can also easily expose user privacy.Aiming at efficiently and securely sharing data from multiple parties and avoiding leakage of user private information, the development of related research and technologies on the non-aggregated data sharing field was introduced.Firstly, secure multi-party computing and its technologies were briefly described, including homomorphic encryption, oblivious transfer, secret sharing, etc.Secondly, the federated learning architecture was analyzed from the aspects of source data nodes and transmission optimization.Finally, the existing non-aggregated data sharing frameworks were listed and compared.In addition, the challenges and future potential research directions were summarized, such as complex multi-party scenarios, the balance between optimization and cost, as well as related security risks.

Key words: privacy protection, data sharing, federated learning, secure multi-party computation

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