Enhancing privacy policy comprehension through Privacify: A user-centric approach using advanced language models

Abstract

As the digital age advances, the collection, usage, and dissemination of personal data have become critical concerns for users, regulators, and the cybersecurity community. Questions surrounding the extent of identifiable data collection, its usage, sharing, selling, and the mechanisms of consent are increasingly central to discussions on user data privacy. These issues highlight the need for effective management and comprehension of privacy policies. To this end, this paper introduces Privacify— a production-ready web application designed to enhance the accessibility and understandability of privacy policies, thus empowering users to make more informed decisions about their data. At its backend, Privacify leverages a combination of text segmentation, summarization using Large Language Model (LLM), and map-reduce technologies to facilitate BASE analysis for single-document insights and WRT and REV for comprehensive cross-document analysis. Designed with a user-centric approach, Privacify features an intuitive interface that presents all relevant user privacy information in easy-to-understand language, complete with a detailed explainability component. This design not only simplifies privacy policies but also aids users in effortlessly navigating complex privacy terms, significantly boosting their ability to protect and manage their personal information. Our evaluation employs robust methodologies, including reliability and accuracy assessments, alongside rigorous functionality verification through ROUGE metrics and human analysis, validating the system’s efficacy and performance. Privacify’s architecture promotes scalability, replicability, and seamless deployment, advancing the domain of user data protection through improved privacy comprehension.