Title: PrivacyContext: identifying malicious mobile privacy leak using program context

Authors: Xiaolei Wang; Yuexiang Yang

Addresses: College of Computer, National University of Defense Technology, Changsha, 410073, China ' College of Computer, National University of Defense Technology, Changsha, 410073, China

Abstract: Serious concerns have been raised about user's privacy leak in mobile apps, and many detection approaches are proposed. To evade detection, new mobile malware starts to mimic privacy-related behaviours of benign apps, and mix malicious privacy leak with benign ones to reduce the chance of being observed. Since prior proposed approaches primarily focus on the privacy leak discovery, these evasive techniques will make differentiating between malicious and benign privacy disclosures difficult during privacy leak analysis. In this paper, we propose PrivacyContext to identify malicious privacy leak using context. PrivacyContext can be used to purify privacy leak detection results for automatic and easy interpretation by filtering benign privacy disclosures. Experiments show PrivacyContext can perform an effective and efficient static privacy disclosure analysis enhancement and identify malicious privacy leak with 92.73% true positive rate. Evaluation also indicates that to keep the accuracy of privacy disclosure classification, our proposed contexts are all necessary.

Keywords: privacy leak; context; activation event; dependent operation; sources; sinks.

DOI: 10.1504/IJICS.2019.103054

International Journal of Information and Computer Security, 2019 Vol.11 No.6, pp.562 - 584

Received: 05 Aug 2016
Accepted: 01 Jun 2017
Published online: 15 Oct 2019 *

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