Differentially private sequence generative adversarial networks for data privacy masking

Yu ZHANG; Xixiang LYU; Yucong ZOU; Yige LI

doi:10.11959/j.issn.2096-109x.2020046

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Differentially private sequence generative adversarial networks for data privacy masking

Papers | 更新时间：2024-06-03

- Differentially private sequence generative adversarial networks for data privacy masking
- Chinese Journal of Network and Information Security Vol. 6, Issue 4, Pages: 109-119(2020)
- 作者机构：
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- DOI：10.11959/j.issn.2096-109x.2020046
  CLC： TP309.2
- Online First：2020-08，
  
  Published：15 August 2020
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Yu ZHANG, Xixiang LYU, Yucong ZOU, et al. Differentially private sequence generative adversarial networks for data privacy masking[J]. Chinese Journal of Network and Information Security, 2020, 6(4): 109-119.
DOI：

Yu ZHANG, Xixiang LYU, Yucong ZOU, et al. Differentially private sequence generative adversarial networks for data privacy masking[J]. Chinese Journal of Network and Information Security, 2020, 6(4): 109-119. DOI： 10.11959/j.issn.2096-109x.2020046.

摘要

基于生成对抗网络和差分隐私提出一种文本序列数据集脱敏模型，即差分隐私文本序列生成网络（DP-SeqGAN）。DP-SeqGAN通过生成对抗网络自动提取数据集的重要特征并生成与原数据分布接近的新数据集，基于差分隐私对模型做随机加扰以提高生成数据集的隐私性，并进一步降低鉴别器过拟合。DP-SeqGAN 具有直观通用性，无须对具体数据集设计针对性脱敏规则和对模型做适应性调整。实验表明，数据集经DP-SeqGAN脱敏后其隐私性和可用性明显提升，成员推断攻击成功率明显降低。

Abstract

Based on generative adversary networks and the differential privacy mechanism

a differentially private sequence generative adversarial net (DP-SeqGAN) was proposed

with which the privacy of text sequence data sets can be filtered out.DP-SeqGAN can be used to automatically extract important features of a data set and then generate a new data set which was close to the original one in terms of data distributions.Based on differential privacy

randomness is introduced to the model

which improves the privacy of the generated data set and further reduces the over fitting of the discriminator.The proposed DP-SeqGAN was universal

so there is no need to adjust the model adaptively for datasets or design complex masking rules against dataset characters.The experiments show that the privacy and usability of a sequence data set are both improved significantly after it is processed by the DP-SeqGAN model

and DP-SeqGAN can greatly reduce the success rate of member inference attacks against the generated data set.

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references

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