Chinese Journal of Network and Information Security ›› 2020, Vol. 6 ›› Issue (4): 109-119.doi: 10.11959/j.issn.2096-109x.2020046

• Papers • Previous Articles     Next Articles

Differentially private sequence generative adversarial networks for data privacy masking

Yu ZHANG,Xixiang LYU(),Yucong ZOU,Yige LI   

  1. School of Cyber Engineering,Xidian University,Xi’an 710071,China
  • Revised:2020-04-09 Online:2020-08-15 Published:2020-08-13
  • Supported by:
    The Foundation of Science and Technology on Information Assurance Laboratory(KJ-17-108);The Key Research and Development Project of Shaanxi Province,China(2019ZDLGY12-08);The National Key R&D Program of China(2018YFB0804105)

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.

Key words: privacy preserving, data privacy masking, generative adversarial network, differential privacy

CLC Number: 

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