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k -Anonymity in practice: How generalisation and suppression affect machine learning classifiers
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DOI：10.1016/j.cose.2021.102488.png)
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Abstract
En 中文
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 tech-niques 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 com-parison 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 depend-ing on the dataset and anonymisation method. Furthermore, Mondrian can be considered as the method with the most appealing properties for subsequent classification. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Keywords:
k-Anonymity
Machine learning
Anonymisation
Generalisation
Suppression
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