#5909. k-Anonymity in practice: How generalisation and suppression affect machine learning classifiers

August 2026publication date
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Computer Science (all);
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Abstract:
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.
Keywords:
Anonymisation; Generalisation; k-Anonymity; Machine learning; Suppression

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