Towards permission request prediction on mobile apps via structure feature learning

Abstract

The popularity of mobile apps has posed severe privacy risks to users because many permissions are over-claimed. In this work, we explore the techniques that can automatically predict the permission requests of a new mobile app based on its functionality and textual description information, which can help users to be aware of the privacy risks of mobile apps. Our framework formalizes the permission prediction problem as a multi-label learning problem, where a regularized structure feature learning framework is utilized to automatically capture the relations among textual descriptions, permissions, and app category. The permission prediction result can be automatically learned using our approach. We evaluate our approach on 173 permission requests from 11, 067 mobile apps across 30 categories. Extensive experiment results indicate that our method consistently provides better performance (3%-5% performance improvement in terms of Fl score), when compared to the other state-of-the-art methods.