Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification
Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training data. This makes them especially attractive for auditing models deployed in sensitive domains such as healthcare or finance. For these protocols to be meaningful in real-world audit settings, though, their guarantees must reflect how the model will behave once deployed, rather than merely certifying its behavior during an audit. Existing security definitions often miss this mark: most certify model behavior only on a fixed audit dataset, without ensuring that the same guarantees generalize to other datasets drawn ...
| Authors | Carter Luck, Olive Franzese-McLaughlin, Elisaweta Masserova et al. |
|---|---|
| Primary category | cs.CR |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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