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arXiv · 1706.02562

Pain-Free Random Differential Privacy with Sensitivity Sampling

Benjamin I. P. Rubinstein·Francesco Aldà
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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 $(\epsilon,\delta)$-differentially private under bounded global sensitivity automatically achieves $(\epsilon,\delta,\gamma)$-random differential privacy (Hall et al., 2012), without any target-specific calculations required. We demonstrate on worked example learners how our usable approach adopts a naturally-relaxed privacy guarantee, while achieving more accurate releases even for non-private functions that are black-box computer programs.

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BibTeXRIS

Benjamin I. P. Rubinstein, Francesco Aldà. 2017-06-08. Pain-Free Random Differential Privacy with Sensitivity Sampling. https://arxiv.org/abs/1706.02562

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