《计算机应用》唯一官方网站 ›› 2023, Vol. 43 ›› Issue (12): 3647-3653.DOI: 10.11772/j.issn.1001-9081.2022121881
• 人工智能 • 下一篇
收稿日期:
2022-12-26
修回日期:
2023-03-19
接受日期:
2023-03-24
发布日期:
2023-04-12
出版日期:
2023-12-10
通讯作者:
蔡英
作者简介:
张宇(1997—),女,河北石家庄人,硕士研究生,主要研究方向:深度学习、差分隐私基金资助:
Yu ZHANG, Ying CAI(), Jianyang CUI, Meng ZHANG, Yanfang FAN
Received:
2022-12-26
Revised:
2023-03-19
Accepted:
2023-03-24
Online:
2023-04-12
Published:
2023-12-10
Contact:
Ying CAI
About author:
ZHANG Yu, born in 1997, M. S. candidate. Her research interests include deep learning, differential privacy.Supported by:
摘要:
针对卷积神经网络(CNN)模型的训练过程中,模型参数记忆数据部分特征导致的隐私泄露问题,提出一种CNN中基于差分隐私的动量梯度下降算法(DPGDM)。首先,在模型优化的反向传播过程中对梯度添加满足差分隐私的高斯噪声,并用加噪后的梯度值参与模型参数的更新过程,从而实现对模型整体的差分隐私保护;其次,为了减少引入差分隐私噪声对模型收敛速度的影响,设计学习率衰减策略,改进动量梯度下降算法;最后,为了降低噪声对模型准确率的影响,在模型优化过程中动态地调整噪声尺度的值,从而改变在每一轮迭代中需要对梯度加入的噪声量。实验结果表明,与DP-SGD (Differentially Private Stochastic Gradient Descent)相比,所提算法可以在隐私预算为0.3和0.5时,模型准确率分别提高约5和4个百分点。可见,所提算法提高了模型的可用性,并实现了对模型的隐私保护。
中图分类号:
张宇, 蔡英, 崔剑阳, 张猛, 范艳芳. 卷积神经网络中基于差分隐私的动量梯度下降算法[J]. 计算机应用, 2023, 43(12): 3647-3653.
Yu ZHANG, Ying CAI, Jianyang CUI, Meng ZHANG, Yanfang FAN. Gradient descent with momentum algorithm based on differential privacy in convolutional neural network[J]. Journal of Computer Applications, 2023, 43(12): 3647-3653.
参数名 | 不同实验数据集下的参数值 | ||
---|---|---|---|
MNIST | Fashion-MNIST | CIFAR-10 | |
批处理数据的样本数N | 250 | 256 | 1 500 |
模型训练轮次 | 100 | 100 | 300 |
学习率初始值 | 0.04 | 0.04 | 0.20 |
噪声尺度初始值 | 2 | 2 | 15 |
噪声尺度最小值 | 0.18 | 0.16 | 0.10 |
动量超参数m | 0.9 | 0.9 | 0.9 |
表1 实验参数
Tab. 1 Experimental parameters
参数名 | 不同实验数据集下的参数值 | ||
---|---|---|---|
MNIST | Fashion-MNIST | CIFAR-10 | |
批处理数据的样本数N | 250 | 256 | 1 500 |
模型训练轮次 | 100 | 100 | 300 |
学习率初始值 | 0.04 | 0.04 | 0.20 |
噪声尺度初始值 | 2 | 2 | 15 |
噪声尺度最小值 | 0.18 | 0.16 | 0.10 |
动量超参数m | 0.9 | 0.9 | 0.9 |
算法 | 准确率 | 损失的准确率 |
---|---|---|
NO-PRIVACY | 89.82 | 0.00 |
DPGDM | 85.55 | 4.27 |
DP-SGD | 78.80 | 11.02 |
DP-PSO | 81.40 | 8.42 |
表2 不同算法在Fashion-MNIST数据集上的准确率对比 (%)
Tab.2 Accuracy comparison of different algorithms on Fashion-MNIST dataset
算法 | 准确率 | 损失的准确率 |
---|---|---|
NO-PRIVACY | 89.82 | 0.00 |
DPGDM | 85.55 | 4.27 |
DP-SGD | 78.80 | 11.02 |
DP-PSO | 81.40 | 8.42 |
图5 不同隐私预算下不同算法在Fashion-MNIST数据集上的模型准确率对比
Fig.5 Model accuracy comparison among different algorithms under different privacy budgets on Fashion-MNIST dataset
算法 | 准确率 | 损失的准确率 |
---|---|---|
NO-PRIVACY | 70.12 | 0.00 |
DPGDM | 68.72 | 1.40 |
DP-SGD | 64.59 | 5.53 |
DP-SGD with Tempered Sigmoid[ | 66.20 | 3.92 |
表3 不同算法在CIFAR-10数据集上的准确率对比 (%)
Tab. 3 Accuracy comparison of different algorithms on CIFAR-10 dataset
算法 | 准确率 | 损失的准确率 |
---|---|---|
NO-PRIVACY | 70.12 | 0.00 |
DPGDM | 68.72 | 1.40 |
DP-SGD | 64.59 | 5.53 |
DP-SGD with Tempered Sigmoid[ | 66.20 | 3.92 |
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