A recommender-based system for assisting non technical users in managing Android permissions

Abstract

Today, permissions management solutions on mobile devices employ Identity Based Access Control models. This approach was suitable when people had only few games (like snake or tetris) installed on their mobile phones. Current situa- tion is different. A survey from Google in 2013 showed that, on average, US users have installed 33 applications on their Android smartphones. As a result, these users must manage hundreds of permissions if they want to protect their privacy. Scalability of IBAC is a well known issue and many more advanced access control models have proposed abstractions to cope with this problem. However, such models are more complex to handle for non technical users. Thus, we present a permission management system for Android devices that 1) learns users' privacy preferences, 2) proposes them abstract authorization rules, and 3) provides advanced features to manage these high level rules. We prove this approach is more efficient than current permissions management system by comparing it to Privacy Guard Manager.