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Evidence record 13728 · automatically gathered

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 a

Record details

Published: 23 July 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Bias & fairness · Privacy · Healthcare · Transparency
Retrieved: 27 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (23 July 2026), “Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification,” evidence record 13728, https://ethics.ai/record/13728 (originally published by arXiv cs.CR (AI security)).

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