Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis
Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | European Symposium on Research in Computer Security : Computer Security – ESORICS 2021 |
Volume | 12973 |
Publisher | Springer |
Publication date | 2021 |
ISBN (Print) | 978-3-030-88427-7 |
ISBN (Electronic) | 978-3-030-88428-4 |
DOIs | |
Publication status | Published - 2021 |
Series | Lecture Notes in Computer Science |
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Volume | 12973 |
ISSN | 0302-9743 |
ID: 86280892