Plausible Deniability Guarantees for Whistleblowers
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Merged summary
TL;DR — This is a security/privacy theory paper that gives whistleblowers formal plausible-deniability guarantees by framing audit-selection privacy as differential privacy; it's not about AI/ML advancement, so no listed topic fits.
- Formalizes protection under a strong-adversary threat model (the audited org observes selection decisions) as per-report $(0, δ)$-differential privacy over the transcript of audit selections.
- Proves a negative result: randomized response at the selection step can never beat uniform random auditing by more than $δ$ at any horizon.
- Provides a generic reduction from private auditing to private continual counting; any $(0, δ)$-DP counter plugs in via post-processing, yielding noise scaling of $O(\sqrt{\log T})$ over $T$ decisions.
- Utility theorem: selection error vanishes when the noisy report gap between top and runner-up organizations grows faster than $\sqrt{\log T}$; simulations beat randomized response.
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Plausible Deniability Guarantees for Whistleblowers
Public signals
N/A
TL;DR — This is a security/privacy theory paper that gives whistleblowers formal plausible-deniability guarantees by framing audit-selection privacy as differential privacy; it's not about AI/ML advancement, so no listed topic fits.
- Formalizes protection under a strong-adversary threat model (the audited org observes selection decisions) as per-report $(0, δ)$-differential privacy over the transcript of audit selections.
- Proves a negative result: randomized response at the selection step can never beat uniform random auditing by more than $δ$ at any horizon.
- Provides a generic reduction from private auditing to private continual counting; any $(0, δ)$-DP counter plugs in via post-processing, yielding noise scaling of $O(\sqrt{\log T})$ over $T$ decisions.
- Utility theorem: selection error vanishes when the noisy report gap between top and runner-up organizations grows faster than $\sqrt{\log T}$; simulations beat randomized response.