Privacy-preserving method for face recognition based on homomorphic encryption.
In recent years, facial recognition technology has been widely adopted in modern society. However, the plaintext storage, computation, and transmission of facial data have posed significant risks of information leakage. To address this issue, this paper proposes a facial recognition framework based...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314656 |
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| _version_ | 1850144390120472576 |
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| author | Zhigang Song Gong Wang Wenqin Yang Yunliang Li Yinsheng Yu Zeli Wang Xianghan Zheng Yang Yang |
| author_facet | Zhigang Song Gong Wang Wenqin Yang Yunliang Li Yinsheng Yu Zeli Wang Xianghan Zheng Yang Yang |
| author_sort | Zhigang Song |
| collection | DOAJ |
| description | In recent years, facial recognition technology has been widely adopted in modern society. However, the plaintext storage, computation, and transmission of facial data have posed significant risks of information leakage. To address this issue, this paper proposes a facial recognition framework based on approximate homomorphic encryption (HE_FaceNet), aimed at effectively mitigating privacy leaks during the facial recognition process. The framework first utilizes a pre-trained model to extract facial feature templates, which are then encrypted. The encrypted templates are matched using Euclidean distance, with the final recognition being performed after decryption. However, the time-consuming nature of homomorphic encryption calculations limits the practical applicability of the HE_FaceNet framework. To overcome this limitation, this paper introduces an optimization scheme based on clustering algorithms to accelerate the facial recognition process within the HE_FaceNet framework. By grouping similar faces into clusters through clustering analysis, the efficiency of searching encrypted feature values is significantly improved. Performance analysis indicates that the HE_FaceNet framework successfully protects facial data privacy while maintaining high recognition accuracy, and the optimization scheme demonstrates high accuracy and significant computational efficiency across facial datasets of varying sizes. |
| format | Article |
| id | doaj-art-db6fa260c7244855bde7faab7c54ab07 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-db6fa260c7244855bde7faab7c54ab072025-08-20T02:28:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031465610.1371/journal.pone.0314656Privacy-preserving method for face recognition based on homomorphic encryption.Zhigang SongGong WangWenqin YangYunliang LiYinsheng YuZeli WangXianghan ZhengYang YangIn recent years, facial recognition technology has been widely adopted in modern society. However, the plaintext storage, computation, and transmission of facial data have posed significant risks of information leakage. To address this issue, this paper proposes a facial recognition framework based on approximate homomorphic encryption (HE_FaceNet), aimed at effectively mitigating privacy leaks during the facial recognition process. The framework first utilizes a pre-trained model to extract facial feature templates, which are then encrypted. The encrypted templates are matched using Euclidean distance, with the final recognition being performed after decryption. However, the time-consuming nature of homomorphic encryption calculations limits the practical applicability of the HE_FaceNet framework. To overcome this limitation, this paper introduces an optimization scheme based on clustering algorithms to accelerate the facial recognition process within the HE_FaceNet framework. By grouping similar faces into clusters through clustering analysis, the efficiency of searching encrypted feature values is significantly improved. Performance analysis indicates that the HE_FaceNet framework successfully protects facial data privacy while maintaining high recognition accuracy, and the optimization scheme demonstrates high accuracy and significant computational efficiency across facial datasets of varying sizes.https://doi.org/10.1371/journal.pone.0314656 |
| spellingShingle | Zhigang Song Gong Wang Wenqin Yang Yunliang Li Yinsheng Yu Zeli Wang Xianghan Zheng Yang Yang Privacy-preserving method for face recognition based on homomorphic encryption. PLoS ONE |
| title | Privacy-preserving method for face recognition based on homomorphic encryption. |
| title_full | Privacy-preserving method for face recognition based on homomorphic encryption. |
| title_fullStr | Privacy-preserving method for face recognition based on homomorphic encryption. |
| title_full_unstemmed | Privacy-preserving method for face recognition based on homomorphic encryption. |
| title_short | Privacy-preserving method for face recognition based on homomorphic encryption. |
| title_sort | privacy preserving method for face recognition based on homomorphic encryption |
| url | https://doi.org/10.1371/journal.pone.0314656 |
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