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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