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DP-GAN: Differentially private consecutive data publishing using generative adversarial nets
Journal of Network and Computer Applications ( IF 8.7 ) Pub Date : 2021-04-20 , DOI: 10.1016/j.jnca.2021.103066
Stella Ho , Youyang Qu , Bruce Gu , Longxiang Gao , Jianxin Li , Yong Xiang

In the era of big data, increasingly massive volumes of data is generated and published consecutively for both research and commercial purposes. The potential value of sensitive information also attracts interest from adversaries and thereby arises public concern. Current research mostly focuses on privacy-preserving data publishing in a statistic manner rather than taking the dynamics and correlation of context into consideration. Motivated by this, we propose a novel idea that combining differential privacy and generative adversarial nets. Generative adversarial nets and its extensions are used to generate a synthetic dataset with indistinguishable statistic features while differential privacy guarantees a trade-off between privacy protection and data utility. By employing a min-max game with three players, we devise a deep generative model, namely DP-GAN model, for synthetic data generation while fulfilling the privacy constraints in a differentially private manner. Extensive simulation results on a real-world dataset testify the superiority of the proposed model in terms of privacy protection, data utility, and efficiency.



中文翻译:

DP-GAN:使用生成对抗网络的差分私人连续数据发布

在大数据时代,为了研究和商业目的,越来越多的海量数据被连续生成和发布。敏感信息的潜在价值也吸引了对手的兴趣,从而引起了公众的关注。当前的研究主要集中在以统计方式发布保护隐私的数据,而不是考虑上下文的​​动态性和相关性。因此,我们提出了一种新颖的想法,将差异隐私和生成对抗网络相结合。生成对抗网络及其扩展用于生成具有不可区分的统计特征的综合数据集,而差异性隐私则保证了隐私保护与数据实用性之间的权衡。通过使用由三名玩家组成的最小-最大游戏,我们设计了一种深度生成模型,即DP-GAN模型,用于合成数据生成,同时以不同的私有方式满足隐私约束。真实数据集上的大量仿真结果证明了该模型在隐私保护,数据实用性和效率方面的优越性。

更新日期:2021-04-23
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