Robust and private Bayesian inference
Paper i proceeding, 2014

We examine the robustness and privacy of Bayesian inference, under assumptions on the prior, and with no modifications to the Bayesian framework. First, we generalise the concept of differential privacy to arbitrary dataset distances, outcome spaces and distribution families. We then prove bounds on the robustness of the posterior, introduce a posterior sampling mechanism, show that it is differentially private and provide finite sample bounds for distinguishability-based privacy under a strong adversarial model. Finally, we give examples satisfying our assumptions.

Författare

Christos Dimitrakakis

Chalmers, Data- och informationsteknik, Datavetenskap

Blaine Nelson

Universität Potsdam

Aikaterini Mitrokotsa

Chalmers, Data- och informationsteknik, Nätverk och system

B. Rubinstein

University of Melbourne

Lecture Notes in Computer Science

0302-9743 (ISSN)

Vol. 8776 291-305

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

DOI

10.1007/978-3-319-11662-4_21

ISBN

978-3-319-11662-4

Mer information

Senast uppdaterat

2018-03-19