Disclosure of data analytics has important scientific and commercial
justifications. However, no data shall be disclosed without a diligent
investigation of risks posed for privacy of subjects. Do data analysts have the
right tools to perform such investigations? Privug is a tool-supported method
to explore information leakage properties of programs producing the analytics
to be disclosed. It uses classical off-the-shelf tools for Bayesian
programming, reinterpreting a regular program probabilistically. This in turn
allows information-theoretic analysis of program behavior. For privacy
researchers, the method provides a fast and lightweight way to experiment with
privacy protection measures and mechanisms. We demonstrate that Privug is
accurate, scalable, and applicable. We show how to use it to explore parameters
of differential privacy, and how to benefit from a range of leakage estimators.

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