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A Privacy Protection Approach Based on Android Application's Runtime Behavior Monitor and Control
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Author(s): Fan Wu (Beijing University of Posts and Telecommunications, Beijing, China), Ran Sun (Beijing University of Posts and Telecommunications, Beijing, China), Wenhao Fan (Beijing University of Posts and Telecommunications, Beijing, China), Yuan'An Liu (Beijing University of Posts and Telecommunications, Beijing, China), Feng Liu (State Key Laboratory of Information Security, Institute of Information Engineering and School of Cybersecurity University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China)and Hui Lu (Guangzhou University, Guangzhou, China)
Copyright: 2020
Pages: 20
Source title:
Cyber Warfare and Terrorism: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2466-4.ch022
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Abstract
This article proposes a system that focuses on Android application runtime behavior forensics. Using Linux processes, a dynamic injection and a Java function hook technology, the system is able to manipulate the runtime behavior of applications without modifying the Android framework and the application's source code. Based on this method, a privacy data protection policy that reflects users' intentions is proposed by extracting and recording the privacy data usage in applications. Moreover, an optimized random forest algorithm is proposed to reduce the policy training time. The result shows that the system realizes the functions of application runtime behavior monitor and control. An experiment on 134 widely used applications shows that the basic privacy policy could satisfy the majority of users' privacy intentions.
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