Navigating Privacy Policies with NLP and Graph Mining: Advancements in User-Centric Legal Document Analysis

As mobile, healthcare, shopping, gaming, and other sectors become increasingly popular, users are frequently required to consent to privacy policies that explain how their personal information will be collected, used, and shared. However, these policies are often lengthy and complex, making it difficult for users to fully understand them, which leaves many users unaware of potential risks to their privacy and data security. This research paper addresses the growing need for privacy-conscious software across various sectors, setting the stage for future developments in accountable and transparent system design. This article investigates the application of Natural Language Processing, graph mining, and knowledge graph analysis as transformative tools to enhance the analysis of privacy policies. This study explores how the current state-of-the-art research enhances the comprehensibility and transparency of PP documents, ultimately empowering users to understand their privacy rights and how their data may be shared. We focus on automating the summarization and simplification of PPs to make them more user-friendly. Our approach includes flagging potential issues that may help identify risks, generating simplified summaries of policies, and enabling users to make informed decisions about how their personal data is used. This initiative aims to build trust in the management of personal data.