Differentially Private Bayesian Inference for Exponential Families
Machine Learning
2018-10-29 v3 Cryptography and Security
Machine Learning
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.
Keywords
Cite
@article{arxiv.1809.02188,
title = {Differentially Private Bayesian Inference for Exponential Families},
author = {Garrett Bernstein and Daniel Sheldon},
journal= {arXiv preprint arXiv:1809.02188},
year = {2018}
}
Comments
NIPS 2018. Code available at https://github.com/gbernstein6/private_bayesian_expfam