Using Text Mining To Infer The Purpose Of Permission Use In Mobile Apps

Understanding the purpose of why sensitive data is used could help improve privacy as well as enable new kinds of access control. In this paper, we introduce a new technique for inferring the purpose of sensitive data usage in the context of Android smartphone apps. We extract multiple kinds of features from decompiled code, focusing on app-specific features and text-based features. These features are then used to train a machine learning classifier. We have evaluated our approach in the context of two sensitive permissions, namely ACCESS FINE LOCATION and READ CONTACT LIST, and achieved an accuracy of about 85% and 94% respectively in inferring purposes. We have also found that text-based features alone are highly effective in inferring purposes.