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Practical Considerations of Fully Homomorphic Encryption in Privacy-Preserving Machine Learning
Conference proceeding   Peer reviewed

Practical Considerations of Fully Homomorphic Encryption in Privacy-Preserving Machine Learning

Dan Chia-Tien Lo, Yong Shi, Hossain Shahriar, Bobin Deng, Xinyue Zhang and Mei-Lan Chen
IEEE International Conference on Big Data, pp.6330-6335
IEEE International Conference on Big Data (BigData) (Washington, DC, USA, 12/15/2024–12/18/2024)
12/15/2024
DOI: https://doi.org/10.1109/BigData62323.2024.10825068
Web of Science ID: WOS:001451321806057

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Abstract

fully homomorphic encryption privacy-preserving Big Data Machine Learning
Machine learning has been successfully applied to big data analytics across various disciplines. However, as data is collected from diverse sectors, much of it is private and confidential. At the same time, one of the major challenges in machine learning is the slow training speed of large models, which often requires high-performance servers or cloud services. To protect data privacy while still allowing model training on such servers, privacy-preserving machine learning using Fully Homomorphic Encryption (FHE) has gained significant attention. However, its widespread adoption is hindered by performance degradation. This paper presents our experiments on training models over encrypted data using FHE. The results show that while FHE ensures privacy, it can significantly degrade performance, requiring complex tuning to optimize.

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