差分隐私GAN梯度裁剪阈值的自适应选取方法

郭鹏; 钟尚平; 陈开志; 程航

doi:10.11959/j.issn.2096-109x.2018041

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差分隐私GAN梯度裁剪阈值的自适应选取方法

学术论文 | 更新时间：2024-06-03

- 差分隐私GAN梯度裁剪阈值的自适应选取方法
- Adaptive selection method of differential privacy GAN gradient clipping thresholds
- 网络与信息安全学报 2018年4卷第5期页码：10-20
- 作者机构：
  
  1. 福州大学数学与计算机科学学院，福建福州 350116
  2. 网络系统信息安全福建省高校重点实验室，福建福州 350116
- 作者简介：
  
  [ "郭鹏（1991-），男，河南郸城人，福州大学硕士生，主要研究方向为信息安全、机器学习。" ]
  [ "钟尚平（1969-），男，福建武平人，福州大学教授，主要研究方向为信息安全，机器学习、智能图像分析。" ]
  [ "陈开志（1983-），男，福建福州人，福州大学讲师，主要研究方向为信息安全、图像安全。" ]
  [ "程航（1979-），男，福建连江人，福州大学副教授，主要研究方向为多媒体安全、加密域图像检索、图像处理。" ]
- 基金信息：
  
  福建省自然科学基金资助项目;The Natural Science Foundation of Fujian Province(2017J01502);福州大学博士科研基金资助项目;The Scientific Research Foundation of Fuzhou University(XRC-17015)
- DOI：10.11959/j.issn.2096-109x.2018041
  中图分类号： TP309
- 网络首发：2018-05，
  
  纸质出版：2018-05-15
- 稿件说明：
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郭鹏, 钟尚平, 陈开志, 等. 差分隐私GAN梯度裁剪阈值的自适应选取方法[J]. 网络与信息安全学报, 2018,4(5):10-20.

Peng GUO, Shangping ZHONG, Kaizhi CHEN, et al. Adaptive selection method of differential privacy GAN gradient clipping thresholds[J]. Chinese Journal of Network and Information Security, 2018, 4(5): 10-20.
郭鹏, 钟尚平, 陈开志, 等. 差分隐私GAN梯度裁剪阈值的自适应选取方法[J]. 网络与信息安全学报, 2018,4(5):10-20. DOI： 10.11959/j.issn.2096-109x.2018041.

Peng GUO, Shangping ZHONG, Kaizhi CHEN, et al. Adaptive selection method of differential privacy GAN gradient clipping thresholds[J]. Chinese Journal of Network and Information Security, 2018, 4(5): 10-20. DOI： 10.11959/j.issn.2096-109x.2018041.

摘要

提出自适应选取差分隐私 GAN 梯度裁剪阈值的方法。该方法假设可以接触到与隐私数据同分布的小部分公开数据，通过从公开数据中随机选取一批数据，设置裁剪阈值为这批数据的平均梯度范数，迭代上述操作直到网络聚合。在 Mnist 和 Cifar10 数据集上对所提方法进行了实验验证，结果表明，在合理隐私预算下与差分隐私辅助分类GAN相比，卷积神经网络（CNN）分类器准确率提高1%~4%，而评估分数（inception scores）提升0.6~1.2。

Abstract

A method of adaptive selection of differential privacy GAN gradient clipping threshold was proposed.The method assumes that a small portion of public data that is identically distributed with the private data can be accessed

a batch of data is randomly selected from the public data

a clipping threshold is set as an average gradient norm of the batch of data

and the above operations are iterated until the network converges.The method was verified on the Mnist and Cifar10 data sets.The results show that under a reasonable privacy budget

the accuracy of CNN classifiers is improved by 1%~4% compared with the differential privacy auxiliary classifier GAN

inception scores increased by 0.6~1.2.

关键词

Keywords

references

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