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2024 | OriginalPaper | Chapter

Privacy-Preserving Tree-Based Inference with TFHE

Authors : Jordan Frery, Andrei Stoian, Roman Bredehoft, Luis Montero, Celia Kherfallah, Benoit Chevallier-Mames, Arthur Meyre

Published in: Mobile, Secure, and Programmable Networking

Publisher: Springer Nature Switzerland

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Abstract

Fully Homomorphic Encryption is a powerful tool for processing encrypted data and is particularly adapted to the type of programs that are common in machine learning (ML). On tabular data, tree-based ML models obtain state-of-the-art results, are more robust, and are easier to use and deploy than neural networks. We introduce an implementation of privacy-preserving decision tree evaluation based on the TFHE scheme, leveraging optimized representations for encrypted integer and TFHE’s powerful programmable bootstrapping mechanism. Our technique is applicable to decision trees, random forests, and gradient boosted trees. We demonstrate our approach on popular datasets and show that accuracy on encrypted data is very close the one obtained by the same models applied to clear data, while latency is competitive with the state of the art.

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Title: Privacy-Preserving Tree-Based Inference with TFHE
Authors: Jordan Frery
Andrei Stoian
Roman Bredehoft
Luis Montero
Celia Kherfallah
Benoit Chevallier-Mames
Arthur Meyre
Publisher: Springer Nature Switzerland
Book: Mobile, Secure, and Programmable Networking
Print ISBN: 978-3-031-52425-7

Electronic ISBN: 978-3-031-52426-4

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-52426-4_10

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