Feasibility of Random Forest with Fully Homomorphic Encryption Applied to Network Data

Shusaku Uemura, Kazuhide Fukushima

2024

Abstract

Random forests are powerful and interpretable machine learning models. Such models are used for analyzing data in various fields. To protect privacy, many methods have been proposed to evaluate random forests with fully homomorphic encryption (FHE), which enables operations such as addition and multiplication under the encryption. In this paper, we focus on the feasibility of random forests with FHE applied to network data. We conducted experiments with random forests with FHE on IoT device classification for three types of bits and nine types of depths. By exponential regressions on the results, we obtained the relations between computation time and depths. This result enables us to estimate the computation time for deeper models.

Paper Citation

in Harvard Style

Uemura S. and Fukushima K. (2024). Feasibility of Random Forest with Fully Homomorphic Encryption Applied to Network Data. In Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-683-5, SciTePress, pages 534-545. DOI: 10.5220/0012394600003648

in Bibtex Style

@conference{icissp24,
author={Shusaku Uemura and Kazuhide Fukushima},
title={Feasibility of Random Forest with Fully Homomorphic Encryption Applied to Network Data},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2024},
pages={534-545},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012394600003648},
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 - Feasibility of Random Forest with Fully Homomorphic Encryption Applied to Network Data
SN - 978-989-758-683-5
AU - Uemura S.
AU - Fukushima K.
PY - 2024
SP - 534
EP - 545
DO - 10.5220/0012394600003648
PB - SciTePress