On the Differential Privacy of Bayesian Inference
Author(s)
Date issued
December 22, 2015
In
AAAI Conference on Artificial Intelligence (AAAI)
From page
2365
To page
2371
Subjects
cs.AI cs.CR cs.LG math.ST stat.ML stat.TH
Abstract
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.
Event name
30th AAAI 2016
Location
Phoenix, Arizona, USA
Publication type
conference paper
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1512.06992.pdf
Type
Main Article
Size
494.18 KB
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Adobe PDF
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