# PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification

arXiv: https://arxiv.org/abs/2404.19744 · HTML: https://mlsanity.com/paper/2404.19744/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [LLMs Accelerate Annotation for Medical Information Extraction](https://mlsanity.com/paper/2312.02296/) (2023)
- [KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction](https://mlsanity.com/paper/2403.07969/) (2024)
- [LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge](https://mlsanity.com/paper/2401.10036/) (2024)
- [The Privacy Policy Landscape After the GDPR](https://mlsanity.com/paper/1809.08396/) (2018)
- [Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data](https://mlsanity.com/paper/2308.03107/) (2023)
- [MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks](https://mlsanity.com/paper/2404.00457/) (2024)
- [RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement](https://mlsanity.com/paper/1905.02497/) (2019)
- [Data Capsule: A New Paradigm for Automatic Compliance with Data Privacy Regulations](https://mlsanity.com/paper/1909.00077/) (2019)

## Built on by (most cited first, 2 of 2)

- [Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning](https://mlsanity.com/paper/2604.27713/) (2026)
- [Differentially Private Synthetic Data Generation Using Context-Aware GANs](https://mlsanity.com/paper/2512.08869/) (2025)

## Abstract (arXiv)

Data protection and privacy is becoming increasingly crucial in the digital era. Numerous companies depend on third-party vendors and service providers to carry out critical functions within their operations, encompassing tasks such as data handling and storage. However, this reliance introduces potential vulnerabilities, as these vendors' security measures and practices may not always align with the standards expected by regulatory bodies. Businesses are required, often under the penalty of law, to ensure compliance with the evolving regulatory rules. Interpreting and implementing these regulations pose challenges due to their complexity. Regulatory documents are extensive, demanding significant effort for interpretation, while vendor-drafted privacy policies often lack the detail required for full legal compliance, leading to ambiguity. To ensure a concise interpretation of the regulatory requirements and compliance of organizational privacy policy with said regulations, we propose a Large Language Model (LLM) and Semantic Web based approach for privacy compliance. In this paper, we develop the novel Privacy Policy Compliance Verification Knowledge Graph, PrivComp-KG. It is designed to efficiently store and retrieve comprehensive information concerning privacy policies, regulatory frameworks, and domain-specific knowledge pertaining to the legal landscape of privacy. Using Retrieval Augmented Generation, we identify the relevant sections in a privacy policy with corresponding regulatory rules. This information about individual privacy policies is populated into the PrivComp-KG. Combining this with the domain context and rules, the PrivComp-KG can be queried to check for compliance with privacy policies by each vendor against relevant policy regulations. We demonstrate the relevance of the PrivComp-KG, by verifying compliance of privacy policy documents for various organizations.