Conformal Privacy Auditing: Calibrated Re-identification Attacks with Statistical Guarantees
Empirical identity leakage from released text is increasingly driven by attackers that combine large language models (LLMs) with auxiliary knowledge to link documents to individuals. Existing audits typically report success rates for specific attack pipelines but lack finite-sample statistical guarantees, while training-time protections such as differential privacy are difficult to translate into release-time decisions for individual natural-language documents. We introduce Conformal Privacy Auditing(CPA), a distribution-free calibration framework that provides a statistical certificate of re-identification risk for each released document against LLM-empowered adversaries. CPA outputs a c...
| Authors | Shuo Huang, Gholamreza Haffari, Xingliang Yuan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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