Robust and private Bayesian inference
Paper in proceedings, 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.

Author

Christos Dimitrakakis

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Blaine Nelson

University of Potsdam

Aikaterini Mitrokotsa

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

B. Rubinstein

University of Melbourne

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 8776 291-305

Areas of Advance

Information and Communication Technology

Subject Categories

Probability Theory and Statistics

Computer Science

DOI

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

ISBN

978-3-319-11662-4

More information

Latest update

3/19/2018