| Issue |
Security and Safety
Volume 4, 2025
Security and Safety of Data in Cloud Computing
|
|
|---|---|---|
| Article Number | 2024012 | |
| Number of page(s) | 7 | |
| Section | Other Fields | |
| DOI | https://doi.org/10.1051/sands/2024012 | |
| Published online | 18 October 2024 | |
Views
Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption
Ant Group, Beijing, 100081, China
* Corresponding authors (email: vince.hc@antgroup.com)
Received:
31
July
2024
Revised:
9
September
2024
Accepted:
10
September
2024
Fully Homomorphic Encryption (FHE), known for its ability to process encrypted data without decryption, is a promising technique for solving privacy concerns in the machine learning era. However, there are many kinds of available FHE schemes and way more FHE-based solutions in the literature, and they are still fast evolving, making it difficult to get a complete view. This article aims to introduce recent representative results of FHE-based privacy-preserving machine learning, helping users understand the pros and cons of different kinds of solutions, and choose an appropriate approach for their needs.
Key words: Homomorphic Encryption / Fully Homomorphic Encryption / Machine learning / Privacy-preserving machine learning
Citation: Hong C. Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption. Security and Safety 2025; 4: 2024012. https://doi.org/10.1051/sands/2024012
© The Author(s) 2025. Published by EDP Sciences and China Science Publishing & Media Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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