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Disclosure of data analytics results has important scientific and commercialjustifications. However, no data shall be disclosed without a diligentinvestigation of risks for privacy of subjects. Privug is a tool-supportedmethod to explore information leakage properties of data analytics andanonymization programs. In Privug, we reinterpret a program probabilistically,using off-the-shelf tools for Bayesian inference to performinformation-theoretic analysis of the information flow. For privacyresearchers, Privug provides a fast, lightweight way to experiment with privacyprotection measures and mechanisms. We show that Privug is accurate, scalable,and applicable to a range of leakage analysis scenarios.

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