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隐私保护机器学习的密码学方法

蒋瀚 刘怡然 宋祥福 王皓 郑志华 徐秋亮

蒋瀚, 刘怡然, 宋祥福, 王皓, 郑志华, 徐秋亮. 隐私保护机器学习的密码学方法[J]. 电子与信息学报, 2020, 42(5): 1068-1078. doi: 10.11999/JEIT190887
引用本文: 蒋瀚, 刘怡然, 宋祥福, 王皓, 郑志华, 徐秋亮. 隐私保护机器学习的密码学方法[J]. 电子与信息学报, 2020, 42(5): 1068-1078. doi: 10.11999/JEIT190887
Han JIANG, Yiran LIU, Xiangfu SONG, Hao WANG, Zhihua ZHENG, Qiuliang XU. Cryptographic Approaches for Privacy-Preserving Machine Learning[J]. Journal of Electronics and Information Technology, 2020, 42(5): 1068-1078. doi: 10.11999/JEIT190887
Citation: Han JIANG, Yiran LIU, Xiangfu SONG, Hao WANG, Zhihua ZHENG, Qiuliang XU. Cryptographic Approaches for Privacy-Preserving Machine Learning[J]. Journal of Electronics and Information Technology, 2020, 42(5): 1068-1078. doi: 10.11999/JEIT190887

隐私保护机器学习的密码学方法

doi: 10.11999/JEIT190887
基金项目: 国家自然科学基金(61632020, 61572294);山东省自然科学基金(ZR2017MF021);山东省科技重大创新工程项目(2018CXGC0702);山东半岛国家自主创新示范区发展建设项目(S190101010001)
详细信息
    作者简介:

    蒋瀚:男,1974年生,讲师,研究方向为密码学与信息安全

    刘怡然:女,1996年生,博士生,研究方向为密码学与信息安全

    宋祥福:男,1992年生,博士生,研究方向为密码学与信息安全

    王皓:男,1984年生,副教授,研究方向为密码学与信息安全

    郑志华:女,1962年生,副教授,研究方向为密码学与信息安全

    徐秋亮:男,1960年生,教授,研究方向为密码学与信息安全

    通讯作者:

    徐秋亮 xql@sdu.edu.cn

  • 中图分类号: TN918; TP309

Cryptographic Approaches for Privacy-Preserving Machine Learning

Funds: The National Natural Science Foundation of China (61632020, 61572294); The Natural Science Foundation of Shandong Province (ZR2017MF021); The Major Innovation Project of Science and Technology of Shandong Province (2018CXGC0702); The Funds Project of National Independent Innovation Demonstration Zone in Shandong Peninsula (S190101010001)
  • 摘要: 新一代人工智能技术的特征,表现为借助GPU计算、云计算等高性能分布式计算能力,使用以深度学习算法为代表的机器学习算法,在大数据上进行学习训练,来模拟、延伸和扩展人的智能。不同数据来源、不同的计算物理位置,使得目前的机器学习面临严重的隐私泄露问题,因此隐私保护机器学习(PPM)成为目前广受关注的研究领域。采用密码学工具来解决机器学习中的隐私问题,是隐私保护机器学习重要的技术。该文介绍隐私保护机器学习中常用的密码学工具,包括通用安全多方计算(SMPC)、隐私保护集合运算、同态加密(HE)等,以及应用它们来解决机器学习中数据整理、模型训练、模型测试、数据预测等各个阶段中存在的隐私保护问题的研究方法与研究现状。
  • 图  1  机器学习的一般过程

    图  2  Sigmoid函数与分段激活函数图像

    图  3  联邦学习

    图  4  安全数据聚合协议

    表  1  MiniONN效率实验结果

    数据集MNISTCIFAR10
    精确度(%)99.5291.5
    运行时间(s)32011686
    数据传输(MB)336.71803
    #p/h1638402524
    下载: 导出CSV

    表  2  文献[42]效率分析

    通信计算存储
    用户端$O\left( {\left( {\lambda + \mu } \right)m + nr} \right)$$O\left( {m{ {\left( {\lg\left( m \right)} \right)}^2} + \left( {l + 1} \right) \cdot c\left( {r,n} \right)} \right)$$O\left( {4k\lambda + \mu \left( {m + 3\left\lceil {\dfrac{l}{2} } \right\rceil } \right) + nr} \right)$
    服务器端$O\left( { {m^2}\mu + nmr + \dfrac{ {ml} }{2} } \right)$$O\left( {{m^2} + \left( {m - \zeta } \right) \cdot l \cdot c\left( {r,n} \right)} \right)$$O\left( {mnr + {m^2}\mu + \dfrac{ {ml\mu } }{2} } \right)ght)$
    下载: 导出CSV
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    出版历程
    • 收稿日期:  2019-11-06
    • 修回日期:  2020-03-08
    • 网络出版日期:  2020-04-03
    • 刊出日期:  2020-06-04

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