University College London · 2 ´Ecole Polytechnique de Montr´eal · 3Mila - Qu´ebec Artificial Intelligence Institute
Whistleblowers are a key safeguard against organizational wrongdoing, but the threat of retaliation deters reporting. Existing whistleblower-protection proposals lack formal privacy guarantees, and existing differential privacy mechanisms do not directly target the natural threat model -- one in which the audited organization itself observes auditor selection decisions and uses them to identify reporters. We formalize protection against a strong-adversary threat model as per-report
(0,δ)-differential privacy on the transcript of audit selections. Within this framework we prove that a natural approach -- randomized response applied at the selection step -- can never outperform uniform random auditing by more than
δ at any horizon. We then give a generic mechanism that reduces private auditing to private continual counting: any
(0,δ)-DP continual counter plugs in by post-processing, and the audit transcript inherits the same per-report guarantee. Instantiating the reduction with a recent work in continual counting yields per-report
(0,δ)-DP with noise scaling as
O(logT) across a horizon of
T audit decisions. A utility theorem shows that the selection error vanishes whenever the noisy report gap between the most-reported organization and the runner-up grows faster than
logT. Simulations show a substantial improvement over randomized response.