Comprehensive Quantitative Analysis on Privacy Leak Behavior

Lejun Fan, Yuanzhuo Wang, Xiaolong Jin, Jingyuan Li, Xueqi Cheng and Shuyuan Jin

PLOS ONE, 2013, vol. 8, issue 9, 1-10

Abstract: Privacy information is prone to be leaked by illegal software providers with various motivations. Privacy leak behavior has thus become an important research issue of cyber security. However, existing approaches can only qualitatively analyze privacy leak behavior of software applications. No quantitative approach, to the best of our knowledge, has been developed in the open literature. To fill this gap, in this paper we propose for the first time four quantitative metrics, namely, possibility, severity, crypticity, and manipulability, for privacy leak behavior analysis based on Privacy Petri Net (PPN). In order to compare the privacy leak behavior among different software, we further propose a comprehensive metric, namely, overall leak degree, based on these four metrics. Finally, we validate the effectiveness of the proposed approach using real-world software applications. The experimental results demonstrate that our approach can quantitatively analyze the privacy leak behaviors of various software types and reveal their characteristics from different aspects.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0073410

DOI: 10.1371/journal.pone.0073410

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