Differentially Private Bayesian Inference for Exponential Families
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.
Cite
Text
Bernstein and Sheldon. "Differentially Private Bayesian Inference for Exponential Families." Neural Information Processing Systems, 2018.Markdown
[Bernstein and Sheldon. "Differentially Private Bayesian Inference for Exponential Families." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/bernstein2018neurips-differentially/)BibTeX
@inproceedings{bernstein2018neurips-differentially,
title = {{Differentially Private Bayesian Inference for Exponential Families}},
author = {Bernstein, Garrett and Sheldon, Daniel R.},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {2919-2929},
url = {https://mlanthology.org/neurips/2018/bernstein2018neurips-differentially/}
}