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Rényi Differential Privacy Mechanisms for Posterior Sampling

2 October 2017
J. Geumlek
Shuang Song
Kamalika Chaudhuri
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Abstract

Using a recently proposed privacy definition of R\ényi Differential Privacy (RDP), we re-examine the inherent privacy of releasing a single sample from a posterior distribution. We exploit the impact of the prior distribution in mitigating the influence of individual data points. In particular, we focus on sampling from an exponential family and specific generalized linear models, such as logistic regression. We propose novel RDP mechanisms as well as offering a new RDP analysis for an existing method in order to add value to the RDP framework. Each method is capable of achieving arbitrary RDP privacy guarantees, and we offer experimental results of their efficacy.

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