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

• URL: http://arxiv.org/abs/2105.10117v1
• Date: Fri, 21 May 2021 03:59:29 GMT
• Title: Towards Automatic Comparison of Data Privacy Documents: A Preliminary Experiment on GDPR-like Laws
• Authors: Kornraphop Kawintiranon and Yaguang Liu
• Abstract summary: General Data Protection Regulation (NLP) becomes standard law for protection in many countries. 12 countries adopt their similarities-like regulations, but evaluating differences is time-consuming and needs manual effort from legal experts. In this paper, we investigate a simple natural language processing (NLP) approach to tackle the problem.
• Score: 1.3537117504260623
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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