论文标题
预测GDPR罚款的数量
Predicting the Amount of GDPR Fines
论文作者
论文摘要
一般数据保护法规（GDPR）于2018年实施。在执行此次执行之后，欧盟国家数据保护当局已经施加了许多罚款。本文探讨了执行决策中引用的单个GDPR文章，并预测了执行罚款的数量，并从执行决策文件中提取的可用元数据和文本挖掘功能。根据结果，与一般原则，合法性和信息安全有关的文章一直是最常见的文章。尽管罚款的数量在所引用的文章中有所不同，但这三个特定的文章并不脱颖而出。此外，即使使用简单的机器学习技术用于回归分析，也可以实现良好的预测。与文本挖掘功能相比，基本的元数据（例如所引用的文章和原籍国）的性能稍好一些。
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.
