A Usage-Pattern Perspective for Privacy Ranking of Android Apps

Xiaolei Li, Xinshu Dong, Zhenkai Liang

Abstract: Android applies a permission-based model to regulate applications (apps). When users grant apps permissions to access their sensitive data, they cannot control how the apps utilize the data. Existing taint-based techniques only detect the presence of exfiltration flow for the sensitive data, but cannot detect how much sensitive data are leaked. Users need more intuitive measures to inform them which apps are going to leak more of their private information. In this paper, we take an alternative approach for identifying apps’ internal logic about how they utilize the sensitive data. We define such logic as a sequence of operations on the sensitive data, named as the data usage pattern. We build a static analysis tool to automatically extract data usage patterns from Android apps. Our evaluation shows that our approach effectively and efficiently identifies the key operations and thus ranks Android apps according to different usage patterns.