Pain-free random differential privacy with sensitivity sampling

Abstract

Popular approaches to differential privacy, such as the Laplace and exponential mechanisms, calibrate randomised smoothing through global sensitivity of the target non-private function. Bounding such sensitivity is often a prohibitively complex analytic calculation. As an alternative, we propose a straightforward sampler for estimating sensitivity of non-private mechanisms. Since our sensitivity estimates hold with high probability, any mechanism that would be (e, <5)- differentially private under bounded global sensitivity automatically achieves (e, S, 7)-random differential privacy (Hall et al., 2012), without any target-specific calculations required. We demonstrate on worked example lear..