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Differentially Private Bayesian Inference for Exponential Families
Garrett Bernstein · Daniel Sheldon

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #58

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.

Author Information

Garrett Bernstein (University of Massachusetts Amherst)
Daniel Sheldon (University of Massachusetts Amherst)

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