LAUSR

The increase in popularity and users of the Android platform in recent years has led to a lot of innovative and smart Android applications (apps). Many of these apps are highly interactive, customizable, and require user data to provide services. While being convenient, user privacy is the primary concern. It is not guaranteed that these apps are not storing user data for their need or scrapping algorithms through them. Android uses the system of permissions to provide security and protect user data. The user can grant permission for requested resources either at runtime or during the installation process. However, this system is often misused in practice by demanding extra permissions that are not required to provide services. These kinds of apps stop functioning if all permissions are not granted to them. Therefore, in this paper, a privacy preserved secure framework is proposed to prevent an app from stealing user data by restricting all unnecessary permissions. Unnecessary permissions are recognized by predicting the permissions required by a given app by using collaborative filtering and frequent permission set mining algorithms. Thus, the proposed model interacts with the target application and modifies the permission data inside. Experimental results reveal that the proposed model not only protects the user data but also ensures the proper functioning of the given application.