@inproceedings{95da214d830941ba88cddcbb7fd4b2f1,
title = "Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis",
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.",
keywords = "Data Analytics, Privacy Protection, Information Leakage, Anonymization Programs, Bayesian Inference, Data Analytics, Privacy Protection, Information Leakage, Anonymization Programs, Bayesian Inference",
author = "Ra{\'u}l Pardo and Willard Rafnsson and Christian Probst and Andrzej Wasowski",
year = "2021",
doi = "10.1007/978-3-030-88428-4_21",
language = "English",
isbn = "978-3-030-88427-7",
volume = "12973",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
booktitle = "European Symposium on Research in Computer Security",
address = "Germany",
}