Automated Privacy Policy Annotation with Information Highlighting Made Practical Using Deep Representations

Abstract

The privacy policy statements are the primary mean for service providers to inform Internet users about their data collection and use practices, although they often are long and lack a specific structure. In this work, we introduce TLDR, a pipeline that employs various deep representation techniques for normalizing policies through learning and modeling, and an automated ensemble classifier for privacy policy classification. TLDR advances the state-of-the-art by (i) categorizing policy contents into nine privacy policy categories with high accuracy, (ii) detecting missing information in privacy policies, and (iii) significantly reducing policy reading time and improving understandability by users.