Mining Patterns of Sensitive Data Usage.

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Bibliographic Details
Title:	Mining Patterns of Sensitive Data Usage.
Authors:	Avdiienko, Vitalii¹ avdiienko@cs.uni-saarland.de
Source:	ICSE: International Conference on Software Engineering. 2015, p891-894. 4p.
Subjects:	Application software research, Android (Operating system), Mobile apps, Data mining, Data plans
Abstract:	When a user downloads an Android application from a market, she does not know much about its actual behavior. A brief description, a set of screenshots, and the list of permissions, which give a high level intuition of what the application might be doing, are all the user sees before installing and running the application on his device. These elements are not enough to decide whether the application is secure, and for sure they do not indicate whether it might violate the user's privacy by leaking some sensitive data. The goal of my thesis is to employ both static and dynamic taint analyses to gather information on how Android applications use sensitive data. The main hypothesis of this work is that malicious and benign mobile applications differ in how they use sensitive data, and consequently information flow can be used effectively to identify malware. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	When a user downloads an Android application from a market, she does not know much about its actual behavior. A brief description, a set of screenshots, and the list of permissions, which give a high level intuition of what the application might be doing, are all the user sees before installing and running the application on his device. These elements are not enough to decide whether the application is secure, and for sure they do not indicate whether it might violate the user's privacy by leaking some sensitive data. The goal of my thesis is to employ both static and dynamic taint analyses to gather information on how Android applications use sensitive data. The main hypothesis of this work is that malicious and benign mobile applications differ in how they use sensitive data, and consequently information flow can be used effectively to identify malware. [ABSTRACT FROM AUTHOR]
DOI:	10.1109/ICSE.2015.285