$k$ -实践中的相似性：泛化和抑制如何影响机器学习分类器
$k$-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers
摘要：
保护私人信息是数据驱动的研究和商业环境中的一个关键问题。通常，引入匿名化或（选择性）删除等技术是为了允许数据共享，例如：G.在合作研究的情况下。对于匿名化技术，$k$ -匿名标准是最流行的标准之一，有许多关于不同算法和指标的科学出版物。匿名化技术通常需要更改数据，因此必然会影响在底层数据上训练的机器学习模型的结果。在这项工作中，我们进行了系统的比较和详细的调查不同的$k$ -匿名化算法对机器学习模型的结果的影响。我们研究了一组流行的$k$ -匿名化算法与不同的分类器，并评估他们在不同的现实世界的数据集。我们的系统评估表明，随着越来越强的$k$ -匿名约束，分类性能通常会下降，但在不同程度上，强烈依赖于数据集和匿名方法。此外，蒙德里安可以被认为是最有吸引力的属性为后续分类的方法。
英文摘要：
The protection of private information is a crucial issue in data-driven research and business contexts. Typically, techniques like anonymisation or (selective) deletion are introduced in order to allow data sharing, e. g. in the case of collaborative research endeavours. For use with anonymisation techniques, the $k$-anonymity criterion is one of the most popular, with numerous scientific publications on different algorithms and metrics. Anonymisation techniques often require changing the data and thus necessarily affect the results of machine learning models trained on the underlying data. In this work, we conduct a systematic comparison and detailed investigation into the effects of different $k$-anonymisation algorithms on the results of machine learning models. We investigate a set of popular $k$-anonymisation algorithms with different classifiers and evaluate them on different real-world datasets. Our systematic evaluation shows that with an increasingly strong $k$-anonymity constraint, the classification performance generally degrades, but to varying degrees and strongly depending on the dataset and anonymisation method. Furthermore, Mondrian can be considered as the method with the most appealing properties for subsequent classification.
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