# Unveiling Privacy Policy Complexity: An Exploratory Study Using Graph Mining, Machine Learning, and Natural Language Processing

arXiv: https://arxiv.org/abs/2507.02968 · HTML: https://mlsanity.com/paper/2507.02968/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning](https://mlsanity.com/paper/1802.02561/) (2018)
- [PoliGraph: Automated Privacy Policy Analysis using Knowledge Graphs (Journal Version)](https://mlsanity.com/paper/2210.06746/) (2022)
- [The Challenges and Impact of Privacy Policy Comprehension](https://mlsanity.com/paper/2005.08967/) (2020)

## Abstract (arXiv)

Privacy policy documents are often lengthy, complex, and difficult for non-expert users to interpret, leading to a lack of transparency regarding the collection, processing, and sharing of personal data. As concerns over online privacy grow, it is essential to develop automated tools capable of analyzing privacy policies and identifying potential risks. In this study, we explore the potential of interactive graph visualizations to enhance user understanding of privacy policies by representing policy terms as structured graph models. This approach makes complex relationships more accessible and enables users to make informed decisions about their personal data (RQ1). We also employ graph mining algorithms to identify key themes, such as User Activity and Device Information, using dimensionality reduction techniques like t-SNE and PCA to assess clustering effectiveness. Our findings reveal that graph-based clustering improves policy content interpretability. It highlights patterns in user tracking and data sharing, which supports forensic investigations and identifies regulatory non-compliance. This research advances AI-driven tools for auditing privacy policies by integrating interactive visualizations with graph mining. Enhanced transparency fosters accountability and trust.