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支持数据隐私保护的联邦深度神经网络模型研究

张泽辉 富瑶 高铁杠

张泽辉, 富瑶, 高铁杠. 支持数据隐私保护的联邦深度神经网络模型研究. 自动化学报, 2022, 48(5): 1273−1284 doi: 10.16383/j.aas.c200236
引用本文: 张泽辉, 富瑶, 高铁杠. 支持数据隐私保护的联邦深度神经网络模型研究. 自动化学报, 2022, 48(5): 1273−1284 doi: 10.16383/j.aas.c200236
Zhang Ze-Hui, Fu Yao, Gao Tie-Gang. Research on federated deep neural network model for data privacy preserving. Acta Automatica Sinica, 2022, 48(5): 1273−1284 doi: 10.16383/j.aas.c200236
Citation: Zhang Ze-Hui, Fu Yao, Gao Tie-Gang. Research on federated deep neural network model for data privacy preserving. Acta Automatica Sinica, 2022, 48(5): 1273−1284 doi: 10.16383/j.aas.c200236

支持数据隐私保护的联邦深度神经网络模型研究

doi: 10.16383/j.aas.c200236
基金项目: 国家科技重大专项(2018YFB0204304), 天津市研究生科研创新基金资助项目(2019YJSB067)资助
详细信息
    作者简介:

    张泽辉:南开大学软件学院博士研究生. 2019年获得武汉理工大学硕士学位. 主要研究方向为联邦学习, 故障诊断和智能船舶控制. E-mail: zhangtianxia918@163.com

    富瑶:南开大学软件学院硕士研究生. 主要研究方向为云端数据完整性验证, 信息安全. E-mail: FuYao_TJ@163.com

    高铁杠:南开大学软件学院教授. 1991年获得华中理工大学应用数学专业硕士学位, 2005年获得南开大学博士学位. 主要研究方向为联邦学习, 图像水印, 信息隐藏和云端数据安全. 本文通信作者. E-mail: gaotiegang@nankai.edu.cn

Research on Federated Deep Neural Network Model for Data Privacy Preserving

Funds: Supported by National Science and Technology Major Project of China (2018YFB0204304), and Tianjin Research Innovation Project for Postgraduate Students (2019YJSB067)
More Information
    Author Bio:

    ZHANG Ze-Hui Ph.D. candidate at the College of Software, Nankai University. He received his master degree from Wuhan University of Technology in 2019. His research interest covers federated learning, fault diagnosis, and intelligent ship control

    FU Yao Master student at the College of Software, Nankai University. Her research interest covers cloud data integrity verification and information security

    GAO Tie-Gang Professor at the College of Software, Nankai University. He received his master degree in applied mathematics from Huazhong University of Science and Technology in 1991, and Ph.D. degree from Nankai University in 2005. His research interest covers federated learning, image watermarking, information hiding, and cloud data security. Corresponding author of this paper

  • 摘要: 近些年, 人工智能技术已经在图像分类、目标检测、语义分割、智能控制以及故障诊断等领域得到广泛应用, 然而某些行业(例如医疗行业)由于数据隐私的原因, 多个研究机构或组织难以共享数据训练联邦学习模型. 因此, 将同态加密(Homomorphic encryption, HE)算法技术引入到联邦学习中, 提出一种支持数据隐私保护的联邦深度神经网络模型(Privacy-preserving federated deep neural network, PFDNN). 该模型通过对其权重参数的同态加密保证了数据的隐私性, 并极大地减少了训练过程中的加解密计算量. 通过理论分析与实验验证, 所提出的联邦深度神经网络模型具有较好的安全性, 并且能够保证较高的精度.
  • 图  1  联邦学习结构

    Fig.  1  Federated learning structure

    图  2  神经网络结构

    Fig.  2  Neural network construction

    图  3  不同比例的数据信息泄露

    Fig.  3  Different proportions of data information leakage

    图  4  不同偏置值的数据信息泄露

    Fig.  4  Data information leakage of different bias values

    图  5  训练过程交互图

    Fig.  5  Interaction in the training process

    图  6  支持数据隐私保护的联邦学习训练过程

    Fig.  6  The training process of the date privacy-preserving federated learning

    图  7  各模型训练过程曲线

    Fig.  7  Training process curves of each models

    图  8  测试集预测结果的混淆矩阵

    Fig.  8  The confounding matrix of the test dataset prediction results

    表  1  训练者与参数服务器获得的数据信息

    Table  1  Data information obtained by the participant and parameter server

    名称训练者 i参数服务器
    中间数据Enc(Wglobal)Enc(Wglobal)
    Prediction resultsEnc(Wpar,1)
    LossiEnc(Wpar,2)
    Gi
    Wpar,iEnc(Wpar,n)
    下载: 导出CSV

    表  2  算法执行时间

    Table  2  Execution time of the algorithms

    操作 参数量
    10163264160512204850176
    Paillier 方案加密0.07 s0.11 s0.23 s0.48 s1.19 s3.91 s15.05 s381.51 s
    解密0.02 s0.03 s0.07 s0.13 s0.34 s1.10 s4.31 s107.77 s
    加法0.10 ms0.19 ms0.49 ms1.09 ms5.59 ms8.07 ms30.82 ms0.81 s
    直接相加0.96 μs0.99 μs1.01 μs1.03 μs1.10 μs1.49 μs2.83 μs33.30 μs
    下载: 导出CSV

    表  3  深度神经网络模型结构

    Table  3  Deep neural network model structure

    model深度神经网络
    结构L1 input = 784, output = 64
    L2 input = 64, output = 32
    L3 input = 32, output = 16
    L4 input = 16, output = 10
    下载: 导出CSV

    表  4  不同模型偏差结果

    Table  4  The deviation results of the different models

    模型 参数
    mini-batch = 32mini-batch = 64mini-batch = 128mini-batch = 256
    DNN-1devavg2.04%2.62%2.68%6.25%
    PFDNN-1devmax0.47%0.67%1.07%2.82%
    DNN-2devavg2.30%3.00%4.39%5.26%
    PFDNN-2devmax0.36%0.57%0.03%0.05%
    下载: 导出CSV

    表  5  不同类别物品的预测结果

    Table  5  Prediction results of the different items

    MethodDNN-1
    mini-batch = 128
    lr = 0.005, epoch = 300
    DNN-1
    mini-batch =128
    lr = 0.01, epoch = 300
    FDNN-1
    mini-batch = 128
    lr = 0.005, epoch = 300
    FDNN-2
    mini-batch = 128
    lr = 0.01, epoch = 300
    TypeRecallPrecisionRecallPrecisionRecallPrecisionRecallPrecision
    T-shirt82.00%83.42%86.00%78.18%78.60%84.24%76.50%86.50%
    Trouser96.40%98.07%98.20%92.99%95.70%98.76%96.10%99.17%
    Pullover78.00%78.23%80.40%76.14%67.60%83.25%76.90%78.47%
    Dress87.70%87.96%80.10%91.23%88.70%85.62%89.30%86.03%
    Coat82.70%77.80%86.10%70.40%82.60%74.08%85.80%75.59%
    Sandal95.50%95.50%94.70%95.75%93.80%95.81%94.90%96.25%
    Shirt68.10%71.16%51.80%80.06%71.40%64.09%68.30%69.48%
    Sneaker94.00%95.05%95.30%92.08%97.10%90.58%97.00%92.21%
    Bag96.50%95.64%96.50%93.78%96.20%95.82%96.80%96.80%
    Boot95.90%93.84%94.30%95.54%93.10%96.38%93.70%96.80%
    Average87.68%87.67%86.34%86.62%86.48%86.86%87.53%87.69%
    下载: 导出CSV
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  • 收稿日期:  2020-04-21
  • 录用日期:  2020-07-27
  • 网络出版日期:  2022-03-18
  • 刊出日期:  2022-05-13

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