Privacy Amplification by Mixing and Diffusion Mechanisms

no code implementations • NeurIPS 2019 • Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek

A fundamental result in differential privacy states that the privacy guarantees of a mechanism are preserved by any post-processing of its output.

Profile-Based Privacy for Locally Private Computations

no code implementations • 21 Jan 2019 • Joseph Geumlek, Kamalika Chaudhuri

Differential privacy has emerged as a gold standard in privacy-preserving data analysis.

Privacy Preserving

Renyi Differential Privacy Mechanisms for Posterior Sampling

no code implementations • NeurIPS 2017 • Joseph Geumlek, Shuang Song, Kamalika Chaudhuri

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.

regression

Rényi Differential Privacy Mechanisms for Posterior Sampling

no code implementations • 2 Oct 2017 • Joseph Geumlek, Shuang Song, Kamalika Chaudhuri

Using a recently proposed privacy definition of R\'enyi Differential Privacy (RDP), we re-examine the inherent privacy of releasing a single sample from a posterior distribution.

regression

On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis

no code implementations • 23 Mar 2016 • James Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri

Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notion of data privacy, under certain conditions (Dimitrakakis et al., 2014; Wang et al., 2015).

Bayesian Inference Privacy Preserving +2