TY - GEN
T1 - Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis
AU - Pardo, Raúl
AU - Rafnsson, Willard
AU - Probst, Christian
AU - Wasowski, Andrzej
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-030-88428-4_21
DO - 10.1007/978-3-030-88428-4_21
M3 - Article in proceedings
SN - 978-3-030-88427-7
VL - 12973
T3 - Lecture Notes in Computer Science
BT - European Symposium on Research in Computer Security
PB - Springer
ER -