Towards Automatic Comparison of Data Privacy Documents: A Preliminary Experiment on GDPR-like Laws

TL;DR

This paper explores an NLP-based method using BERT to automatically compare GDPR-like data privacy laws across different countries, aiming to reduce manual legal analysis effort.

Contribution

It introduces a simple NLP approach leveraging BERT for structured data extraction and similarity measurement of privacy regulations, demonstrating its effectiveness.

Findings

BERT with cosine similarity outperforms baseline models

The approach reduces manual effort in legal document comparison

Data and code are publicly available for further research

Abstract

General Data Protection Regulation (GDPR) becomes a standard law for data protection in many countries. Currently, twelve countries adopt the regulation and establish their GDPR-like regulation. However, to evaluate the differences and similarities of these GDPR-like regulations is time-consuming and needs a lot of manual effort from legal experts. Moreover, GDPR-like regulations from different countries are written in their languages leading to a more difficult task since legal experts who know both languages are essential. In this paper, we investigate a simple natural language processing (NLP) approach to tackle the problem. We first extract chunks of information from GDPR-like documents and form structured data from natural language. Next, we use NLP methods to compare documents to measure their similarity. Finally, we manually label a small set of data to evaluate our approach. The empirical result shows that the BERT model with cosine similarity outperforms other baselines. Our data and code are publicly available.