Automatic Permission Optimization Framework for Privacy Enhancement of Mobile Applications

Mobile applications play a crucial role in the IoT system, which is experiencing unprecedented growth. However, users possessing little knowledge of permission configurations often accept app permission requests without reading them, which opens a backdoor for the potential adversaries to launch the future attacks. Proposing an automatic permission management scheme is an attractive solution to solve this issue, but since users have varying attitudes toward privacy, such a scheme would be neither straightforward nor user friendly. In this study, an automatic permission optimization framework, Permizer, is proposed to recommend different app permission configurations to users with different privacy preferences. Permizer estimates the permission risks and builds the permission-functionality mapping to each app, then regulates the relationship between permission and app functionality. Permizer is the first module to achieve a balance between privacy protection and app functionality under the personal privacy preference condition. Finally, we develop Permizer as a one-button service on the real-world Android OS with 58 apps. Case studies conducted on TikTok and Amazon Alexa also demonstrate its practicability and effectiveness.