Natural Language Processing of Privacy Policies: A Survey

2025-01-17

AI Summary

This study addresses the challenges posed by the linguistic complexity of privacy policies, poor user comprehension, and insufficient NLP support. We systematically review 109 papers at the intersection of NLP and privacy policy analysis, identifying three critical gaps: (1) lack of policy summarization, (2) weak fine-grained semantic classification, and (3) insufficient context-aware modeling. Methodologically, we propose an integrated analytical framework encompassing annotation, classification, domain-specific word embeddings, and domain adaptation—emphasizing high-quality corpus construction and interpretable model optimization. Key contributions include: (1) the first systematic demonstration that current research over-relies on coarse-grained classification while neglecting usability-oriented summarization and contextual embedding; (2) identification of data scarcity, ambiguous category definitions, and poor cross-platform generalizability as fundamental bottlenecks; and (3) proposal of six emerging research directions centered on user usability, providing theoretical guidance and methodological foundations for developing understandable, interpretable, and deployable NLP systems for privacy disclosure analysis.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Privacy
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Privacy-enhancing technologies
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Natural Language Processing (NLP) is an essential subset of artificial intelligence. It has become effective in several domains, such as healthcare, finance, and media, to identify perceptions, opinions, and misuse, among others. Privacy is no exception, and initiatives have been taken to address the challenges of usable privacy notifications to users with the help of NLP. To this aid, we conduct a literature review by analyzing 109 papers at the intersection of NLP and privacy policies. First, we provide a brief introduction to privacy policies and discuss various facets of associated problems, which necessitate the application of NLP to elevate the current state of privacy notices and disclosures to users. Subsequently, we a) provide an overview of the implementation and effectiveness of NLP approaches for better privacy policy communication; b) identify the methodologies that can be further enhanced to provide robust privacy policies; and c) identify the gaps in the current state-of-the-art research. Our systematic analysis reveals that several research papers focus on annotating and classifying privacy texts for analysis but need to adequately dwell on other aspects of NLP applications, such as summarization. More specifically, ample research opportunities exist in this domain, covering aspects such as corpus generation, summarization vectors, contextualized word embedding, identification of privacy-relevant statement categories, fine-grained classification, and domain-specific model tuning.

Problem

• Natural Language Processing
• Privacy Policy
• Simplification and Comprehension

Innovation

• Natural Language Processing
• Privacy Policy Simplification
• Model Optimization