Differentially Private Bayesian Inference for Exponential Families
This addresses privacy concerns for data analysts working with sensitive sources, offering a novel solution for private statistical inference.
The authors tackled the problem of performing Bayesian inference on sensitive data while preserving privacy, presenting the first method for differentially private Bayesian inference in exponential families that accounts for privacy noise and provides properly calibrated posterior beliefs in non-asymptotic regimes.
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.