Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy

Published: 16 Jan 2024, Last Modified: 15 Apr 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Pure Differential Privacy, Monte Carlo sampling, Gaussian Differential Privacy, Exponential Mechanism
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TL;DR: We propose an approximate Monte Carlo sampling method to achieve pure and Gaussian differential privacy.
Abstract: Posterior sampling, i.e., exponential mechanism to sample from the posterior distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees and does not suffer from potentially unbounded privacy breach introduced by $(\varepsilon,\delta)$-approximate DP. In practice, however, one needs to apply approximate sampling methods such as Markov chain Monte Carlo (MCMC), thus re-introducing the unappealing $\delta$-approximation error into the privacy guarantees. To bridge this gap, we propose the Approximate SAample Perturbation (abbr. ASAP) algorithm which perturbs an MCMC sample with noise proportional to its Wasserstein-infinity ($W_\infty$) distance from a reference distribution that satisfies pure DP or pure Gaussian DP (i.e., $\delta=0$). We then leverage a Metropolis-Hastings algorithm to generate the sample and prove that the algorithm converges in W$_\infty$ distance. We show that by combining our new techniques with a localization step, we obtain the first nearly linear-time algorithm that achieves the optimal rates in the DP-ERM problem with strongly convex and smooth losses.
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Primary Area: optimization
Submission Number: 8643