Differentially Private Bayesian Inference for Exponential Families

September 06, 2018 · Entered Twilight · 🏛 Neural Information Processing Systems

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Repo contents: .gitignore, Bounded_Suff_Stats, LICENSE, README.md, Unbounded_Suff_Stats, __init__.py

Authors Garrett Bernstein, Daniel Sheldon arXiv ID 1809.02188 Category cs.LG: Machine Learning Cross-listed cs.CR, stat.ML Citations 48 Venue Neural Information Processing Systems Repository https://github.com/gbernstein6/private_bayesian_expfam ⭐ 1 Last Checked 2 months ago

Abstract

The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources. We present the first method for private Bayesian inference in exponential families that properly accounts for noise introduced by the privacy mechanism. It is efficient because it works only with sufficient statistics and not individual data. Unlike other methods, it gives properly calibrated posterior beliefs in the non-asymptotic data regime.