$k$-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers

TL;DR

This paper systematically examines how various $k$-anonymity algorithms, especially Mondrian, impact machine learning classifier performance across different datasets, highlighting trade-offs between privacy and accuracy.

Contribution

It provides a comprehensive comparison of $k$-anonymisation methods and their effects on classifiers, identifying Mondrian as particularly effective for preserving classification performance.

Findings

Increasing $k$-anonymity generally reduces classification accuracy.

The impact varies significantly depending on the dataset and anonymisation method.

Mondrian maintains better classification performance compared to other algorithms.

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. 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.