Adaptive selection method of differential privacy GAN gradient clipping thresholds

Peng GUO; Shangping ZHONG; Kaizhi CHEN; Hang CHENG

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

您当前的位置：

首页 >

文章列表页 >

Adaptive selection method of differential privacy GAN gradient clipping thresholds

Papers | 更新时间：2024-06-03

- Adaptive selection method of differential privacy GAN gradient clipping thresholds
- Chinese Journal of Network and Information Security Vol. 4, Issue 5, Pages: 10-20(2018)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2018041
  CLC： TP309
- Online First：2018-05，
  
  Published：15 May 2018
- 稿件说明：
移动端阅览
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：

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

SHOKRI R , SHMATIKOV V . Privacy-preserving deep learning [C ] // The 53rd IEEE Annual Allerton Conference on Communication,Control,and Computing . 2015 : 909 - 910 .

ZHU T , LI G , ZHOU W , et al . Differentially private data publishing and analysis:a survey [J ] . IEEE Transactions on Knowledge ＆ Data Engineering , 2017 , 29 ( 8 ): 1619 - 1638 .

MOHASSEL P , ZHANG Y . SecureML:a system for scalable privacy-preserving machine learning [C ] // The 38th IEEE Symposium on Security and Privacy . 2017 : 19 - 38 .

SWEENEY L . K-Anonymity:a model for protecting privacy [J ] . International Journal of UncertainTy Fuzziness and Knowledge Based Systems , 2002 , 10 ( 5 ): 557 - 570 .

MACHANAVAJJHALA A , GEHRKE J , KIFER D , et al . l-diversity:privacy beyond k-anonymity [C ] // The 22nd IEEE International Conference on Data Engineering.Atlanta Georgia . 2006 : 24 - 24 .

LI N , LI T , VENKATASUBRAMANIAN S , et al . T-closeness:privacy beyond k-anonymity and l-diversity [C ] // The 23rd IEEE International Conference on Data Engineering . 2007 : 106 - 115 .

DWORK C . Differential privacy [M ] // Encyclopedia of Cryptography and Security . Springer , 2011 : 338 - 340 .

ABADI M , CHU A , GOODFELLOW I , et al . Deep learning with differential privacy [C ] // The ACM SIGSAC Conference on Computer and Communications Security . 2016 : 308 - 318 .

PAPERNOT N , ABADI M , ERLINGSSON U , et al . Semi-supervised knowledge transfer for deep learning from private training data [C ] // The 5th International Conference on Learning Representations . 2017

ACS G , MELIS L , CASTELLUCCIA C , et al . Differentially private mixture of generative neural networks [C ] // The 17th IEEE International Conference on Data Mining . 2017 : 715 - 720 .

BEAULIEU-JONES B K , WU Z S , WILLIAMS C , et al . Privacy-preserving generative deep neural networks support clinical data sharing [R ] . 2017 .

ZHANG X , JI S , WANG T , et al . Differentially private releasing via Deep Generative Model [EB/OL ] . arXiv preprint arXiv:1801.01594v1 arXiv preprint arXiv:1801.01594v1 . 2018 .

DWORK C , . Differential privacy:a survey of results [C ] // International Conference on Theory and Applications of Models of Computation.Springer,Berlin,Heidelberg . 2008 : 1 - 19 .

DWORK C , ROTH A . The algorithmic foundations of differential privacy [J ] . Foundations and Trends in Theoretical Computer Science , 2014 , 9 ( 3/4 ): 211 - 407 .

GOODFELLOW I , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial nets [C ] // Advances in Neural Information Processing Systems . 2014 : 2672 - 2680 .

ODENA A , OLAH C , SHLENS J . Conditional image synthesis with auxiliary classifier GANs [C ] // International Conference on Machine Learning . 2017 : 2642 - 2651 .

KAIROUZ P , OH S , VISWANATH P , et al . The composition theorem for differential privacy [J ] . IEEE Transactions on Information Theory , 2013 , 63 ( 6 ): 4037 - 4049 .

SALIMANS T , GOODFELLOW I , ZAREMBA W , et al . Improved techniques for training GANs [C ] // Advances in Neural Information Processing Systems . 2016 : 2234 - 2242 .

Views

2630

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰