Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption
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 th...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | Security and Safety |
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| Online Access: | https://sands.edpsciences.org/articles/sands/full_html/2025/01/sands20240021/sands20240021.html |
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| Summary: | 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. |
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| ISSN: | 2826-1275 |