Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis

TL;DR

Privug is a probabilistic programming tool that enables efficient, accurate analysis of privacy risks and information leakage in data analytics and anonymization processes.

Contribution

It introduces a novel approach using probabilistic reinterpretation and Bayesian inference to quantify information leakage in privacy risk analysis.

Findings

Privug is accurate in leakage estimation.

It is scalable to large data and complex programs.

Applicable to diverse privacy analysis scenarios.

Abstract

Disclosure of data analytics results has important scientific and commercial justifications. However, no data shall be disclosed without a diligent investigation of risks for privacy of subjects. Privug is a tool-supported method to explore information leakage properties of data analytics and anonymization programs. In Privug, we reinterpret a program probabilistically, using off-the-shelf tools for Bayesian inference to perform information-theoretic analysis of the information flow. For privacy researchers, Privug provides a fast, lightweight way to experiment with privacy protection measures and mechanisms. We show that Privug is accurate, scalable, and applicable to a range of leakage analysis scenarios.