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On the Differential Privacy of Bayesian Inference
Auteur(s)
Date de parution
2015-12-22T09:22:39Z
In
AAAI Conference on Artificial Intelligence (AAAI)
De la page
2365
A la page
2371
Résumé
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 na{\"i}ve Bayes and Bayesian linear regression illustrate the application of our mechanisms.
Nom de l'événement
30th AAAI 2016
Lieu
Phoenix, Arizona, USA
Identifiants
Type de publication
conference paper
Dossier(s) à télécharger
