Differentially Private Markov Chain Monte Carlo

NeurIPS 2019 Mikko A. HeikkiläJoonas JälköOnur DikmenAntti Honkela

Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning by presenting the first general DP Markov chain Monte Carlo (MCMC) algorithm whose privacy-guarantees are not subject to unrealistic assumptions on Markov chain convergence and that is applicable to posterior inference in arbitrary models... (read more)

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