An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models

Jihyeon Hyeong, Jayoung Kim, Noseong Park, Sushil Jajodia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Tabular data typically contains private and important information; thus, precautions must be taken before they are shared with others. Although several methods (e.g., differential privacy and k-anonymity) have been proposed to prevent information leakage, in recent years, tabular data synthesis models have become popular because they can well trade-off between data utility and privacy. However, recent research has shown that generative models for image data are susceptible to the membership inference attack, which can determine whether a given record was used to train a victim synthesis model. In this paper, we investigate the membership inference attack in the context of tabular data synthesis. We conduct experiments on 4 state-of-the-art tabular data synthesis models under two attack scenarios (i.e., one black-box and one white-box attack), and find that the membership inference attack can seriously jeopardize these models. We next conduct experiments to evaluate how well two popular differentially-private deep learning training algorithms, DP-SGD and DP-GAN, can protect the models against the attack. Our key finding is that both algorithms can largely alleviate this threat by sacrificing the generation quality.

Original languageEnglish
Title of host publicationCIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages4064-4068
Number of pages5
ISBN (Electronic)9781450392365
DOIs
Publication statusPublished - 2022 Oct 17
Event31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States
Duration: 2022 Oct 172022 Oct 21

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/TerritoryUnited States
CityAtlanta
Period22/10/1722/10/21

Bibliographical note

Publisher Copyright:
© 2022 ACM.

All Science Journal Classification (ASJC) codes

  • General Business,Management and Accounting
  • General Decision Sciences

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