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arXiv AI By Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Siew Kei Lam, Anil Anthony Bharath

Subgroup Membership Inference Audits of Differentially Private Synthetic Text

Read the original on arXiv AI →

The paper introduces a subgroup-targeted membership inference game to audit differentially private synthetic text releases, revealing that existing average-case attacks miss significant leakage to vulnerable subgroups. An extensive audit across 32 proxies, four datasets, three generation methods, and five privacy budgets shows that DP reduces overall leakage but leaves concentrated, uneven residual risk, especially for high-risk records. The study demonstrates that which records leak is determined by the release mechanism rather than the records themselves, challenging record-level risk assessment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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