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Differentially Private Markov Chain Monte Carlo
Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Esbo, Finland.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland & Department of Public Health, University of Helsinki, Helsinki, Finland.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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. Our algorithm is based on a decomposition of the Barker acceptance test that allows evaluating the Renyi DP privacy cost of the accept-reject choice. We further show how to improve the DP guarantee through data subsampling and approximate acceptance tests.

Place, publisher, year, edition, pages
2019.
Series
Advances in Neural Information Processing Systems ; 32
Keywords [en]
Differential Privacy, Bayesian Inference, Markov Chain Monte Carlo
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-41239OAI: oai:DiVA.org:hh-41239DiVA, id: diva2:1377690
Conference
33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, December 8-14, 2019
Funder
Academy of Finland, 294238, 303815, 313124Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2020-03-10Bibliographically approved

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Dikmen, Onur

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CiteExportLink to record
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Citation style
  • apa
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