{
  "id": 13728,
  "url": "https://arxiv.org/abs/2607.21839v1",
  "title": "Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification",
  "summary": "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",
  "authors": "Carter Luck, Olive Franzese-McLaughlin, Elisaweta Masserova, Akira Takahashi, Antigoni Polychroniadou, Nicolas Papernot",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T22:06:12.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "x-arxiv-cs-cr-ai-security",
  "source_name": "arXiv cs.CR (AI security)",
  "source_homepage": "https://arxiv.org/list/cs.CR/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13728",
  "original_url": "https://arxiv.org/abs/2607.21839v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}