$k$-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers. (arXiv:2102.04763v2 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2102.04763

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 …

anonymity arxiv learning lg machine machine learning