PrivSense: Static Analysis for Detecting Privacy-Related Code in Mobile Apps
Android application data leakage remains pervasive, despite advancements in leakage detection and user protections. Previous research includes tools that are computa tionally expensive or that did not distinguish between the types of data that were leaked. For example, a user’s age was considered as privacy-sensitive as their pass words. This thesis introduces PrivSense, a light-weight tool designed to detect privacy related code. PrivSense uses Natural Language Processing techniques to capture the semantic meanings of variables, classes, methods, etc., and determines the private data an app is ...
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