用同态加密保护人脸特征，让云端安全计算身份相似度。
CipherFace: A Fully Homomorphic Encryption-Driven Framework for Secure Cloud-Based Facial Recognition
- 用同态加密保护人脸特征，实现云端安全距离计算。
- 支持欧氏与余弦距离的加密计算，准确率损失小于1.5%。
- 适配多种模型和嵌入尺寸，适合隐私敏感的人脸识别场景。
人脸识别系统依赖特征向量表示人脸图像，并通过判断特征间距离是否低于预设阈值来验证身份。尽管特征向量无法逆向还原为原始图像，但仍包含敏感信息，其安全性至关重要。传统加密方法如AES难以在保障安全的前提下利用云算力进行距离计算。同态加密允许对加密数据直接计算，提供了更优解决方案。本文提出CipherFace，一个基于全同态加密的云端人脸识别安全框架，已在http://github.com/serengil/cipherface开源。该框架利用同态加密保护特征隐私的同时，借助云端完成高效距离计算。我们还提出一种新型加密距离计算方法，适用于欧氏距离与余弦距离，解决了加密数据上安全相似性计算的关键挑战。实验涵盖多种人脸识别模型、不同嵌入维度及密码系统配置，验证了CipherFace在真实应用中的可扩展性与有效性。
原文摘要 · Abstract (English)
Facial recognition systems rely on embeddings to represent facial images and determine identity by verifying if the distance between embeddings is below a pre-tuned threshold. While embeddings are not reversible to original images, they still contain sensitive information, making their security critical. Traditional encryption methods like AES are limited in securely utilizing cloud computational power for distance calculations. Homomorphic Encryption, allowing calculations on encrypted data, offers a robust alternative. This paper introduces CipherFace, a homomorphic encryption-driven framework for secure cloud-based facial recognition, which we have open-sourced at http://github.com/serengil/cipherface. By leveraging FHE, CipherFace ensures the privacy of embeddings while utilizing the cloud for efficient distance computation. Furthermore, we propose a novel encrypted distance computation method for both Euclidean and Cosine distances, addressing key challenges in performing secure similarity calculations on encrypted data. We also conducted experiments with different facial recognition models, various embedding sizes, and cryptosystem configurations, demonstrating the scalability and effectiveness of CipherFace in real-world applications.
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