Smart Privacy Policy Assistant: An LLM-Powered System for Transparent and Actionable Privacy Notices

2026-01-09

arXiv.org

AI Summary

This work proposes the first end-to-end large language model (LLM)-based system that addresses the problem of users unknowingly consenting to data usage due to lengthy and opaque privacy policies. The system parses privacy policies in real time, automatically extracts key clauses, assesses associated risk levels, and generates concise, user-friendly explanations to alert individuals before they grant consent. Integrating natural language processing, clause classification, and risk scoring, it is deployed via a browser extension and mobile integration to transparently transform legal text into actionable user information. Experimental results demonstrate strong performance in clause identification accuracy, policy-level risk consistency, and user comprehension, confirming its readiness for real-world deployment.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Most users agree to online privacy policies without reading or understanding them, even though these documents govern how personal data is collected, shared, and monetized. Privacy policies are typically long, legally complex, and difficult for non-experts to interpret. This paper presents the Smart Privacy Policy Assistant, an LLM-powered system that automatically ingests privacy policies, extracts and categorizes key clauses, assigns human-interpretable risk levels, and generates clear, concise explanations. The system is designed for real-time use through browser extensions or mobile interfaces, surfacing contextual warnings before users disclose sensitive information or grant risky permissions. We describe the end-to-end pipeline, including policy ingestion, clause categorization, risk scoring, and explanation generation, and propose an evaluation framework based on clause-level accuracy, policy-level risk agreement, and user comprehension.

Problem

• privacy policies
• user comprehension
• data privacy
• risk communication
• transparency

Innovation

• LLM-powered system
• privacy policy analysis
• risk scoring
• explainable AI
• real-time privacy assistant