Security Evaluation of Decision Tree Meets Data Anonymization

Ryousuke Wakabayashi, Lihua Wang, Ryo Nojima, Ryo Nojima, Atsushi Waseda

2024

Abstract

This paper focuses on the relationship between decision trees, a typical machine learning methods, and data anonymization. We first demonstrate that the information leakage from trained decision trees can be evaluated using well-studied data anonymization techniques. We then show that decision trees can be strengthened against specific attacks using data anonymization techniques. Specifically, we propose two decision tree pruning methods to improve security against uniqueness and homogeneity attacks, and we evaluate the accuracy of these methods experimentally.

Paper Citation

in Harvard Style

Wakabayashi R., Wang L., Nojima R. and Waseda A. (2024). Security Evaluation of Decision Tree Meets Data Anonymization. In Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-683-5, SciTePress, pages 853-860. DOI: 10.5220/0012456600003648

in Bibtex Style

@conference{icissp24,
author={Ryousuke Wakabayashi and Lihua Wang and Ryo Nojima and Atsushi Waseda},
title={Security Evaluation of Decision Tree Meets Data Anonymization},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2024},
pages={853-860},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012456600003648},
isbn={978-989-758-683-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - Security Evaluation of Decision Tree Meets Data Anonymization
SN - 978-989-758-683-5
AU - Wakabayashi R.
AU - Wang L.
AU - Nojima R.
AU - Waseda A.
PY - 2024
SP - 853
EP - 860
DO - 10.5220/0012456600003648
PB - SciTePress