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Privacy for Bayesian modelling

Presented by: 
Anne-Sophie Charest Université Laval
Date: 
Thursday 28th July 2016 - 15:30 to 16:30
Venue: 
INI Seminar Room 2
Abstract: 
The literature now contains a large set of methods to privately estimate parameters from a classical statistical model, or to conduct a data mining or machine learning task. However, little is known about how to perform Bayesian statistics privately. In this talk, I will share my thoughts, and a few results, about ways in which Bayesian modelling could be performed to offer some privacy guarantee. In particular, I will discuss some attempts at sampling from posterior predictive distributions under the constraint of differential privacy (DP). I will also discuss empirical differential privacy, a criterion designed to estimate the DP privacy level offered by a certain Bayesian model, and present some recent results on the meaning and limits of this privacy measure. A lot of what I will present is work in progress, and I am hoping that some of you may want to collaborate with me on this research topic.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons