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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| author | Hong Cheng |
| author_facet | Hong Cheng |
| author_sort | Hong Cheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-03b30cc2471b48f29fea8418d8fd44b6 |
| institution | Kabale University |
| issn | 2826-1275 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Security and Safety |
| spelling | doaj-art-03b30cc2471b48f29fea8418d8fd44b62025-02-07T08:31:11ZengEDP SciencesSecurity and Safety2826-12752025-01-014202401210.1051/sands/2024012sands20240021Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic EncryptionHong Cheng0Ant GroupFully 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.https://sands.edpsciences.org/articles/sands/full_html/2025/01/sands20240021/sands20240021.htmlhomomorphic encryptionfully homomorphic encryptionmachine learningprivacy-preserving machine learning |
| spellingShingle | Hong Cheng Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption Security and Safety homomorphic encryption fully homomorphic encryption machine learning privacy-preserving machine learning |
| title | Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption |
| title_full | Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption |
| title_fullStr | Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption |
| title_full_unstemmed | Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption |
| title_short | Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption |
| title_sort | recent advances of privacy preserving machine learning based on fully homomorphic encryption |
| topic | homomorphic encryption fully homomorphic encryption machine learning privacy-preserving machine learning |
| url | https://sands.edpsciences.org/articles/sands/full_html/2025/01/sands20240021/sands20240021.html |
| work_keys_str_mv | AT hongcheng recentadvancesofprivacypreservingmachinelearningbasedonfullyhomomorphicencryption |