PriFace: a privacy-preserving face recognition framework under untrusted server
- Publisher:
- Springer Nature
- Publication Type:
- Journal Article
- Citation:
- Journal of Ambient Intelligence and Humanized Computing, 2023, 14, (3), pp. 2967-2979
- Issue Date:
- 2023-03-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| s12652-023-04543-7.pdf | Published version | 2.18 MB |
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Alongside the rapid development of artificial intelligence and mobile Internet, face recognition has gradually become a mainstream intelligent terminal authentication technology that is widely applied in security, finance, and social networking, among other fields. However, face images usually contain sensitive personal information, which can have serious consequences for users if this information is leaked or misused. Most existing face privacy protection methods are based on data encryption techniques or differential privacy mechanisms, which make it difficult to strike a good balance between privacy and usability. In this paper, we propose PriFace, a new privacy-preserving method for face recognition based on eigenfaces and locality-sensitive hashing (LSH). PriFace aims to achieve accurate and efficent face recognition with privacy preserved. It applies eigenface technique to reduce the dimension of face images for improving retrieval performance. With the assumption that the server is untrustworthy, PriFace uses the locality-sensitive hashing technique to introduce randomness into the face information for privacy preservation so that the server, or other adversaries, can not reconstruct any user’s face image. Experiments on real-world datasets demonstrate that the proposed PriFace can effectively preserve the privacy of face images and further outperforms the traditional methods.
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