An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models. (arXiv:2208.08114v1 [cs.CR])

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 …

attack data study