Functionality-Based Mobile Application Recommendation System with Security and Privacy Awareness
In this thesis, we propose a functionality-aware system to evaluate and recommend mobile applications with security and privacy awareness. The proposed system has a security layer that evaluates an application and classifies it as being malign or benign. In this way, only applications classified as benign are considered for the functionality-aware recommendation. Also, we employ a technique, called Logical Predicate Mapping (LPM), which allows users to understand the permissions and API calls requested by the app, as well as privacy risks. This information is grouped with other metrics retrieved such as popularity, usability and privacy and shown to users. This way they can decide what to do and understand what can happen.
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