Robust and Private Bayesian Inference
C Dimitrakakis, B Nelson, A Mitrokotsa, B RUBINSTEIN, P Auer, A Clark, T Zeugmann, S Zilles
ALGORITHMIC LEARNING THEORY (ALT 2014) | Springer International Publishing | Published : 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.