On the differential privacy of Bayesian inference
Paper i proceeding, 2016

We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on proba-bilistic graphical models. These include two mechanisms for adding noise to the Bayesian updates, either directly to the posterior parameters, or to their Fourier transform so as to preserve update consistency. We also utilise a recently introduced posterior sampling mechanism, for which we prove bounds for the specific but general case of discrete Bayesian networks; and we introduce a maximum-a-posteriori private mechanism. Our analysis includes utility and privacy bounds, with a novel focus on the influence of graph structure on privacy. Worked examples and experiments with Bayesian naïve Bayes and Bayesian linear regression illustrate the application of our mechanisms.

Författare

Zuhe Zhang

University of Melbourne

B. Rubinstein

University of Melbourne

Christos Dimitrakakis

Chalmers, Data- och informationsteknik, Datavetenskap

30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix Convention CenterPhoenix, United States, 12-17 February 2016

2365-2371

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Sannolikhetsteori och statistik

Datavetenskap (datalogi)

ISBN

978-157735760-5