论文标题
重量：生物识别模板的同态加密融合
HEFT: Homomorphically Encrypted Fusion of Biometric Templates
论文作者
论文摘要
本文提出了一种非相互作用的端到端解决方案，用于使用完全同型加密（FHE）的生物识别模板的安全融合和匹配。给定一对加密的特征向量，我们执行以下密码操作，i）特征串联，ii）通过学习的线性投影，iii）融合和尺寸降低，iii）将标准化为单位$ \ ell_2 $ -norm，以及iv）匹配得分计算。我们的方法称为heft（生物识别模板的同态加密融合），是定制设计的，以克服由FHE施加的独特约束，即缺乏对非偏心操作的支持。从推论的角度来看，我们系统地探索了不同的数据包装方案，以实现计算有效的线性投影，并引入多项式近似进行比例归一化。从训练的角度来看，我们引入了一种fhe-Aware算法，用于学习线性投影矩阵，以减轻近似归一化引起的错误。与各自的Unibimetrictric表示相比，对面部和语音生物识别技术的模板融合和匹配的实验评估表明，将生物特征验证性能提高了11.07％和9.58％的AUROC，同时将特征向量压缩为16（512d至32d），以及（ii）的构图和计算机的范围，并将其构图的尺寸置于16（512d至32d），以及A对数的范围。 1024在884毫秒内。代码和数据可在https://github.com/human-analysis/crypted-biometric-fusion上获得
This paper proposes a non-interactive end-to-end solution for secure fusion and matching of biometric templates using fully homomorphic encryption (FHE). Given a pair of encrypted feature vectors, we perform the following ciphertext operations, i) feature concatenation, ii) fusion and dimensionality reduction through a learned linear projection, iii) scale normalization to unit $\ell_2$-norm, and iv) match score computation. Our method, dubbed HEFT (Homomorphically Encrypted Fusion of biometric Templates), is custom-designed to overcome the unique constraint imposed by FHE, namely the lack of support for non-arithmetic operations. From an inference perspective, we systematically explore different data packing schemes for computationally efficient linear projection and introduce a polynomial approximation for scale normalization. From a training perspective, we introduce an FHE-aware algorithm for learning the linear projection matrix to mitigate errors induced by approximate normalization. Experimental evaluation for template fusion and matching of face and voice biometrics shows that HEFT (i) improves biometric verification performance by 11.07% and 9.58% AUROC compared to the respective unibiometric representations while compressing the feature vectors by a factor of 16 (512D to 32D), and (ii) fuses a pair of encrypted feature vectors and computes its match score against a gallery of size 1024 in 884 ms. Code and data are available at https://github.com/human-analysis/encrypted-biometric-fusion
