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SUMMARY:Privacy for Bayesian modelling - Anne-Sophie Charest (Université 
 Laval)
DTSTART:20160728T143000Z
DTEND:20160728T153000Z
UID:TALK66916@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:The literature now contains a large set of methods to privatel
 y 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 coul
 d be performed to offer some privacy guarantee. In particular\, I will dis
 cuss some attempts at sampling from posterior predictive distributions und
 er the constraint of differential privacy (DP). I will also discuss empiri
 cal differential privacy\, a criterion designed to estimate the DP privacy
  level offered by a certain Bayesian model\, and present some recent resul
 ts 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 t
 o collaborate with me on this research topic.
LOCATION:Seminar Room 2\, Newton Institute
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