# Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies

arXiv: https://arxiv.org/abs/2004.11131 · HTML: https://mlsanity.com/paper/2004.11131/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 22 of 22)

- [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://mlsanity.com/paper/2212.10233/) (2022)
- [Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT Models](https://mlsanity.com/paper/2210.15184/) (2022)
- [Using LLMs for Automated Privacy Policy Analysis: Prompt Engineering, Fine-Tuning and Explainability](https://mlsanity.com/paper/2503.16516/) (2025)
- [Creation and Analysis of an International Corpus of Privacy Laws](https://mlsanity.com/paper/2206.14169/) (2022)
- [On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation](https://mlsanity.com/paper/2402.14052/) (2024)
- [Pre-Trained Language Models for Keyphrase Prediction: A Review](https://mlsanity.com/paper/2409.01087/) (2024)
- [EROS: Entity-Driven Controlled Policy Document Summarization](https://mlsanity.com/paper/2403.00141/) (2024)
- [Entailment-Driven Privacy Policy Classification with LLMs](https://mlsanity.com/paper/2409.16621/) (2024)
- [A Multi-Agent Framework for Mitigating Dialect Biases in Privacy Policy Question-Answering Systems](https://mlsanity.com/paper/2506.02998/) (2025)
- [A Longitudinal Measurement of Privacy Policy Evolution for Large Language Models](https://mlsanity.com/paper/2511.21758/) (2025)
- [More Data Types More Problems: A Temporal Analysis of Complexity, Stability, and Sensitivity in Privacy Policies](https://mlsanity.com/paper/2302.08936/) (2023)
- [Automated Detection and Analysis of Data Practices Using A Real-World Corpus](https://mlsanity.com/paper/2402.11006/) (2024)
- [C3PA: An Open Dataset of Expert-Annotated and Regulation-Aware Privacy Policies to Enable Scalable Regulatory Compliance Audits](https://mlsanity.com/paper/2410.03925/) (2024)
- [Word-level Annotation of GDPR Transparency Compliance in Privacy Policies using Large Language Models](https://mlsanity.com/paper/2503.10727/) (2025)
- [Jurisdiction as Structural Barrier: How Privacy Policy Organization May Reduce Visibility of Substantive Disclosures](https://mlsanity.com/paper/2601.20792/) (2026)
- [PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies](https://mlsanity.com/paper/2603.09214/) (2026)
- [LADFA: A Framework of Using Large Language Models and Retrieval-Augmented Generation for Personal Data Flow Analysis in Privacy Policies](https://mlsanity.com/paper/2601.10413/) (2026)
- [PrivSTRUCT: Untangling Data Purpose Compliance of Privacy Policies in Google Play Store](https://mlsanity.com/paper/2604.22157/) (2026)
- [Enabling Multilingual Privacy Policy Audits: Large-Scale Analysis of Spanish Mobile Apps](https://mlsanity.com/paper/2607.18424/) (2026)
- [SoK: From Generation to Consumption of Privacy Documents in Software Systems](https://mlsanity.com/paper/2608.12511/) (2026)
- [GuardianAgent: Policy-Conditioned Risk-Adaptive Anonymization with Verified Adversarial Escalation](https://mlsanity.com/paper/2608.29251/) (2026)
- [Privacy Washing: Detecting Internal Contradictions in Privacy Policies](https://mlsanity.com/paper/2609.02055/) (2026)

## Abstract (arXiv)

Organisations disclose their privacy practices by posting privacy policies on their website. Even though users often care about their digital privacy, they often don't read privacy policies since they require a significant investment in time and effort. Although natural language processing can help in privacy policy understanding, there has been a lack of large scale privacy policy corpora that could be used to analyse, understand, and simplify privacy policies. Thus, we create PrivaSeer, a corpus of over one million English language website privacy policies, which is significantly larger than any previously available corpus. We design a corpus creation pipeline which consists of crawling the web followed by filtering documents using language detection, document classification, duplicate and near-duplication removal, and content extraction. We investigate the composition of the corpus and show results from readability tests, document similarity, keyphrase extraction, and explored the corpus through topic modeling.