Privacy-Preserving AI Verification via Minimal Information Disclosure
AI verification crosses a trust boundary: a verifier must learn enough to establish an authorized claim, yet the same evidence can reveal sensitive details about the model, workload, or hardware. We introduce minimal information disclosure (MID), which designs and quantifies the information content of verifier-facing evidence itself. MID measures collateral leakage with conditional mutual information: what the release reveals about the protected property after the authorized result is known. MID is general by design: it can accommodate different verification goals, protected properties, evidence sources, and deployment constraints. To demonstrate MID's practicality, we evaluate it on four...
| Authors | Sleem Abdelghafar, Gabriel Kulp |
|---|---|
| Primary category | cs.CR |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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