We propose a privacy-preserving face authentication system for smartphones that guarantees security against malicious clients. Using the proposed system, a face feature vector is stored on a remote server in encrypted form. To guarantee security against an honest-but-curious server who may try to learn the private feature vector, we perform a Euclidean distance-based matching score computation on encrypted feature vectors using homomorphic encryption. To provide security against malicious clients, we adopt a blinding technique. We implement the proposed system on a mobile client and a desktop server. Through an experiment with real-world participants, we demonstrate that secure face verification can be completed in real time (within 1.3 s) even when a smartphone is involved, with an Equal Error Rate (EER) of 3.04%. In further experiments with two public face datasets, CFP and ORL, face verification is completed in approximately 1 s with EER of 1.17% and 0.37%, respectively. Our system is two orders of magnitude faster than previous privacy-preserving face verification method with the same security assumptions and functionalities. To achieve this secure real-time computation, we improve the Catalano-Fiore transformation which converts a linear homomorphic encryption scheme into a quadratic scheme, and parallelize the decryption procedure of our system.

中文翻译：

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适用于智能手机的实用的保护隐私的面部验证可防止恶意客户端

我们为智能手机提出了一种保护隐私的面部认证系统，该系统可确保针对恶意客户端的安全性。使用提出的系统，面部特征向量以加密形式存储在远程服务器上。为了保证针对可能尝试学习私有特征向量的诚实但好奇的服务器的安全性，我们使用同态加密对加密特征向量执行基于欧几里德距离的匹配分数计算。为了提供针对恶意客户端的安全性，我们采用了一种盲法技术。我们在移动客户端和台式机服务器上实施建议的系统。通过与现实世界参与者的实验，我们证明，即使涉及智能手机，安全的面部验证也可以实时（在1.3 s内）完成，均等错误率（EER）为3.04％。在两个公共面部数据集CFP和ORL的进一步实验中，面部验证在大约1秒钟内完成，EER分别为1.17％和0.37％。我们的系统比具有相同安全性假设和功能的以前的隐私保护面部验证方法快两个数量级。为了实现这种安全的实时计算，我们改进了Catalano-Fiore变换，该变换将线性同态加密方案转换为二次方案，并并行化了系统的解密过程。