Rényi Differential Privacy Mechanisms for Posterior Sampling

Published in Advances in Neural Information Processing Systems, 2017

Recommended citation: Geumlek, Joseph, Shuang Song, and Kamalika Chaudhuri (2017). "Rényi differential privacy mechanisms for posterior sampling." Advances in Neural Information Processing Systems. http://papers.nips.cc/paper/7113-renyi-differential-privacy-mechanisms-for-posterior-sampling.pdf

Following from previous work, here we further examine privatized methods for releasing samples from exponential family distributions. Compared to the previous work, here we use Rényi Differential Privacy as the privacy framework. Among other things, this framework permits us to use the stabilizing effect of a prior distribution to increase the privacy guarantees. This work explores how you can achieve privacy either through weakening the Bayesian update of the observations, or through strengthening the prior distribution.

This work was performed at the University of California San Diego.

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