Today, permissions management solutions on mobile devices employ Identity Based Access Control (IBAC) models. If this approach was suitable when people had only a few games (like Snake or Tetris) installed on their mobile phones, the current situation is different. A survey from Google in 2013 showed that, on average, french users have installed 32 applications on their Android smartphones. As a result, these users must manage hundreds of permissions to protect their privacy. Scalability of IBAC is a well-known issue and many more advanced access control models have introduced abstractions to cope with this problem. However, such models are more complex to handle by non-technical users. Thus, we present a permission management system for Android devices that (1) learns users’ privacy preferences with a novel learning algorithm, (2) proposes them abstract authorization rules, and (3) provides advanced features to manage these high-level rules. Our learning algorithm is compared to two other well-known approaches to show its efficiency. Finally, we prove this whole approach is more efficient than current permission management system by comparing it to Privacy Guard Manager.

中文翻译：

---

管理Android权限的新方法：了解用户的偏好

如今，移动设备上的权限管理解决方案采用基于身份的访问控制（IBAC）模型。如果人们只在手机上安装了几款游戏（例如Snake或Tetris），那么这种方法是否合适，当前情况就不同了。Google在2013年进行的一项调查显示，法国用户平均在其Android智能手机上安装了32个应用程序。结果，这些用户必须管理数百个权限以保护其隐私。IBAC的可伸缩性是一个众所周知的问题，许多更高级的访问控制模型已引入抽象来解决此问题。但是，此类模型由非技术用户处理起来更为复杂。因此，我们提出了一种用于Android设备的权限管理系统，该系统（1）通过一种新颖的学习算法来学习用户的隐私偏好，（2）为他们提出抽象的授权规则，（3）提供高级功能来管理这些高级规则。我们的学习算法与其他两种知名方法进行了比较，以显示其效率。最后，通过将其与Privacy Guard Manager进行比较，我们证明了整个方法比当前的权限管理系统更有效。