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

Raúl Pardo; Willard Rafnsson; Christian Probst; Andrzej Wasowski

doi:10.1007/978-3-030-88428-4_21

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

Raúl Pardo, Willard Rafnsson, Christian Probst, Andrzej Wasowski

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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.

Original language	English
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	https://doi.org/10.1007/978-3-030-88428-4_21
Publication status	Published - 2021

Series	Lecture Notes in Computer Science
Volume	12973
ISSN	0302-9743

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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.",

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Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis. / Pardo, Raúl ; Rafnsson, Willard; Probst, Christian et al.
European Symposium on Research in Computer Security: Computer Security – ESORICS 2021. Vol. 12973 Springer, 2021. (Lecture Notes in Computer Science, Vol. 12973).

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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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.

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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 -

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

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