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Authors: Thomas Marchioro 1 ; Lodovico Giaretta 2 ; Evangelos Markatos 1 and Šarūnas Girdzijauskas 2

Affiliations: 1 Institute of Computer Science, Foundation for Research and Technology Hellas, Heraklion, Greece ; 2 Division of Software and Computer Systems, KTH Royal Institute of Technology, Stockholm, Sweden

Keyword(s): Federated Learning, Naive Bayes, Differential Privacy.

Abstract: Growing privacy concerns regarding personal data disclosure are contrasting with the constant need of such information for data-driven applications. To address this issue, the combination of federated learning and differential privacy is now well-established in the domain of machine learning. These techniques allow to train deep neural networks without collecting the data and while preventing information leakage. However, there are many scenarios where simpler and more robust machine learning models are preferable. In this paper, we present a federated and differentially-private version of the Naive Bayes algorithm for classification. Our results show that, without data collection, the same performance of a centralized solution can be achieved on any dataset with only a slight increase in the privacy budget. Furthermore, if certain conditions are met, our federated solution can outperform a centralized approach.


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Paper citation in several formats:
Marchioro, T.; Giaretta, L.; Markatos, E. and Girdzijauskas, Š. (2022). Federated Naive Bayes under Differential Privacy. In Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-590-6; ISSN 2184-7711, SciTePress, pages 170-180. DOI: 10.5220/0011275300003283

author={Thomas Marchioro. and Lodovico Giaretta. and Evangelos Markatos. and Šarūnas Girdzijauskas.},
title={Federated Naive Bayes under Differential Privacy},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT},


JO - Proceedings of the 19th International Conference on Security and Cryptography - SECRYPT
TI - Federated Naive Bayes under Differential Privacy
SN - 978-989-758-590-6
IS - 2184-7711
AU - Marchioro, T.
AU - Giaretta, L.
AU - Markatos, E.
AU - Girdzijauskas, Š.
PY - 2022
SP - 170
EP - 180
DO - 10.5220/0011275300003283
PB - SciTePress