Subgroup Membership Inference Audits of Differentially Private Synthetic Text
Synthetic data releases are increasingly proposed in the literature as a means of sharing realistic data replicas in lieu of sensitive private datasets. Even when the worst-case privacy leakage of such releases is bounded by means of differential privacy (DP), in practice a residual risk remains. Membership inference attack (MIA) audits are conducted to empirically quantify this risk. However, existing methods only measure average-case risk for randomly drawn records, which might conceal the risk to vulnerable subgroups. To highlight this issue, we define a subgroup-targeted membership inference game in which the target pool is an explicit parameter, and instantiate it with an audit of 32...
| Authors | Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar et al. |
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| Primary category | cs.CR |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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