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Joseph Geumlek

2 accepted papers

2019

Privacy Amplification by Mixing and Diffusion Mechanisms

NeurIPS 2019poster

A fundamental result in differential privacy states that the privacy guarantees of a mechanism are preserved by any post-processing of its output. In this paper we investigate under what conditions stochastic post-processing can amplify the privacy of a mechanism. By interpreting post-processing as…

Cited by 49SourcePDFScholar
2017

Renyi Differential Privacy Mechanisms for Posterior Sampling

NeurIPS 2017poster

With the newly proposed privacy definition of Rényi Differential Privacy (RDP) in (Mironov, 2017), 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…