Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale

2025-07-15

AI Summary

Users routinely overlook lengthy privacy policies, resulting in misalignment between stated privacy preferences and platform practices. To address this, we propose PoliAnalyzer—a novel neuro-symbolic framework for privacy policy analysis. Our approach extends the formal Data Usage Language (DUL) to uniformly model both platform policies and multidimensional user profiles, while integrating natural language processing (NLP)-based clause extraction with symbolic logical reasoning to enable interpretable, deterministic compliance verification. Evaluated on the PolicyIE dataset, PoliAnalyzer achieves F1 scores of 90%–100%, accurately detecting high-risk provisions such as “sharing location data with third parties.” Empirical evaluation across real-world policies shows that 95.2% of clauses conform to user preferences, requiring manual review for only 4.8%. This substantially reduces cognitive load and strengthens user agency over personal data.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Data transparency and provenance

Abstract

In modern times, people have numerous online accounts, but they rarely read the Terms of Service or Privacy Policy of those sites despite claiming otherwise. This paper introduces PoliAnalyzer, a neuro-symbolic system that assists users with personalized privacy policy analysis. PoliAnalyzer uses Natural Language Processing (NLP) to extract formal representations of data usage practices from policy texts. In favor of deterministic, logical inference is applied to compare user preferences with the formal privacy policy representation and produce a compliance report. To achieve this, we extend an existing formal Data Terms of Use policy language to model privacy policies as app policies and user preferences as data policies. In our evaluation using our enriched PolicyIE dataset curated by legal experts, PoliAnalyzer demonstrated high accuracy in identifying relevant data usage practices, achieving F1-score of 90-100% across most tasks. Additionally, we demonstrate how PoliAnalyzer can model diverse user data-sharing preferences, derived from prior research as 23 user profiles, and perform compliance analysis against the top 100 most-visited websites. This analysis revealed that, on average, 95.2% of a privacy policy's segments do not conflict with the analyzed user preferences, enabling users to concentrate on understanding the 4.8% (636 / 13205) that violates preferences, significantly reducing cognitive burden. Further, we identified common practices in privacy policies that violate user expectations - such as the sharing of location data with 3rd parties. This paper demonstrates that PoliAnalyzer can support automated personalized privacy policy analysis at scale using off-the-shelf NLP tools. This sheds light on a pathway to help individuals regain control over their data and encourage societal discussions on platform data practices to promote a fairer power dynamic.

Problem

• Automated personalized analysis of privacy policies at scale
• Comparing user preferences with formal privacy policy representations
• Reducing cognitive burden by identifying conflicting policy segments

Innovation

• Neuro-symbolic system for privacy policy analysis
• NLP extracts formal data usage representations
• Logical inference compares user preferences with policies