Predicting the Amount of GDPR Fines

• URL: http://arxiv.org/abs/2003.05151v2
• Date: Mon, 2 Nov 2020 12:34:25 GMT
• Title: Predicting the Amount of GDPR Fines
• Abstract summary: This paper examines individual articles referenced in decisions as well as predicts amount of fines with available metadata text mining features extracted from enforcement documents. Basic meta-data (such as the articles referenced and country origin) yields slightly better performance compared to enforcement features.
• Score: 0.7106986689736826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The General Data Protection Regulation (GDPR) was enforced in 2018. After this enforcement, many fines have already been imposed by national data protection authorities in the European Union (EU). This paper examines the individual GDPR articles referenced in the enforcement decisions, as well as predicts the amount of enforcement fines with available meta-data and text mining features extracted from the enforcement decision documents. According to the results, articles related to the general principles, lawfulness, and information security have been the most frequently referenced ones. Although the amount of fines imposed vary across the articles referenced, these three particular articles do not stand out. Furthermore, good predictions are attainable even with simple machine learning techniques for regression analysis. Basic meta-data (such as the articles referenced and the country of origin) yields slightly better performance compared to the text mining features.

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