Semantic inference from natural language privacy policies and Android code

Abstract

Mobile apps collect dierent categories of personal information to provide users with various services. Companies use privacy policies containing critical requirements to inform users about their data practices. With the growing access to personal information and the scale of mobile app deployment, traceability of links between privacy policy requirements and app code is increasingly important. Automated traceability can be achieved using natural language processing and code analysis techniques. However, such techniques must address two main challenges: ambiguity in privacy policy terminology and unbounded information types provided by users through input elds in GUI. In this work, we propose approaches to interpret abstract terms in privacy policies, identify information types in Android layout code, and create a mapping between them using natural language processing techniques.