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

Autor:	Pardo, Ra��l, Rafnsson, Willard, Probst, Christian, W��sowski, Andrzej
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	FOS: Computer and information sciences Computer Science - Cryptography and Security Cryptography and Security (cs.CR)
Popis:	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. Extended pre-print of the paper "Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis" accepted for publication at the 26th European Symposium on Research in Computer Security (ESORICS) 2021
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85bfd8df731604c8f9e3dda976037f7d http://arxiv.org/abs/2011.08742 Zobrazit plný text záznamu