Comparing Classifiers’ Performance under Differential Privacy

Milan Lopuhaä-Zwakenberg, Mina Alishahi, Jeroen Kivits, Jordi Klarenbeek, Gert-Jan van der Velde, Nicola Zannone

2021

Abstract

The application of differential privacy in privacy-preserving data analysis has gained momentum in recent years. In particular, it provides an effective solution for the construction of privacy-preserving classifiers, in which one party owns the data and another party is interested in obtaining a classifier model from this data. While several approaches have been proposed in the literature to employ differential privacy for the construction of classifiers, an understanding of the difference in performance of these classifiers is currently missing. This knowledge enables the data owner and the analyst to select the most appropriate classification algorithm and training parameters in order to guarantee high privacy requirements while minimizing the loss of accuracy. In this study, we investigate the impact of the use of differential privacy on three well-known classifiers, i.e., Naïve Bayes, SVM, and Decision Tree classifiers. To this end, we show how these classifiers can be trained in a differential privacy setting and perform extensive experiments to evaluate the effect of this privacy enforcement on their performance.

Paper Citation

in Harvard Style

Lopuhaä-Zwakenberg M., Alishahi M., Kivits J., Klarenbeek J., van der Velde G. and Zannone N. (2021). Comparing Classifiers’ Performance under Differential Privacy. In Proceedings of the 18th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-524-1, pages 50-61. DOI: 10.5220/0010519000500061

in Bibtex Style

@conference{secrypt21,
author={Milan Lopuhaä-Zwakenberg and Mina Alishahi and Jeroen Kivits and Jordi Klarenbeek and Gert-Jan van der Velde and Nicola Zannone},
title={Comparing Classifiers’ Performance under Differential Privacy},
booktitle={Proceedings of the 18th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2021},
pages={50-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010519000500061},
isbn={978-989-758-524-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - Comparing Classifiers’ Performance under Differential Privacy
SN - 978-989-758-524-1
AU - Lopuhaä-Zwakenberg M.
AU - Alishahi M.
AU - Kivits J.
AU - Klarenbeek J.
AU - van der Velde G.
AU - Zannone N.
PY - 2021
SP - 50
EP - 61
DO - 10.5220/0010519000500061