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cs.CR2025
Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
Joshua Ward, Xiaofeng Lin, Chi-Hua Wang +1
Tabular Generative Models are often argued to preserve privacy by creating synthetic datasets that resemble training data. However, auditing their empirical privacy remains challen…
cs.CR2025★ 2 cited
Ensembling Membership Inference Attacks Against Tabular Generative Models
Joshua Ward, Yuxuan Yang, Chi-Hua Wang +1
Membership Inference Attacks (MIAs) have emerged as a principled framework for auditing the privacy of synthetic data generated by tabular generative models, where many diverse met…