On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis

Abstract

Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notion of data privacy, under certain conditions (Dimitrakakis et al., 2014; Wang et al., 2015b). While this one posterior sample (OPS) approach elegantly provides privacy “for free… (More)
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@article{Foulds2016OnTT, title={On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis}, author={James R. Foulds and Joseph Geumlek and Max Welling and Kamalika Chaudhuri}, journal={CoRR}, year={2016}, volume={abs/1603.07294} }