arXiv Artificial Intelligence

Subgroup Membership Inference Audits of Differentially Private Synthetic Text

2 hours ago 11.09.2026 07:00 👁 2 views
Subgroup Membership Inference Audits of Differentially Private Synthetic Text

Quick summary

arXiv:2609.09848v1 Announce Type: cross Abstract: 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

Key takeaways

  • arXiv:2609.09848v1 Announce Type: cross Abstract: 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.

Why it matters

The significance is not only the legal text but how it changes product design. Decisions around “Subgroup Membership Inference Audits of Differentially Private Synthetic Text” may reshape data collection, model training, output accountability and market access.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗