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Authors: Milan Lopuhaä-Zwakenberg ; Mina Alishahi ; Jeroen Kivits ; Jordi Klarenbeek ; Gert-Jan van der Velde and Nicola Zannone

Affiliation: Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

Keyword(s): Differential Privacy, Classifier Construction, Accuracy Comparison.

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 i n a differential privacy setting and perform extensive experiments to evaluate the effect of this privacy enforcement on their performance. (More)

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Paper citation in several formats:
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 - SECRYPT; ISBN 978-989-758-524-1; ISSN 2184-7711, SciTePress, pages 50-61. DOI: 10.5220/0010519000500061

@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 - SECRYPT},
year={2021},
pages={50-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010519000500061},
isbn={978-989-758-524-1},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT
TI - Comparing Classifiers’ Performance under Differential Privacy
SN - 978-989-758-524-1
IS - 2184-7711
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
PB - SciTePress

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