We present Blind-Match, a novel biometric identification system that leverages homomorphic encryption (HE) for efficient and privacy-preserving 1:N matching. Blind-Match introduces a HE-optimized cosine similarity computation method, where the key idea is to divide the feature vector into smaller parts for processing rather than computing the entire vector at once. By optimizing the number of these parts, Blind-Match minimizes execution time while ensuring data privacy through HE. Blind-Match achieves superior performance compared to state-of-the-art methods across various biometric datasets. On the LFW face dataset, Blind-Match attains a 99.63% Rank-1 accuracy with a 128-dimensional feature vector, demonstrating its robustness in face recognition tasks. For fingerprint identification, Blind-Match achieves a remarkable 99.55% Rank-1 accuracy on the PolyU dataset, even with a compact 16-dimensional feature vector, significantly outperforming the state-of-the-art method, Blind-Touch, which achieves only 59.17%. Furthermore, Blind-Match showcases practical efficiency in large-scale biometric identification scenarios, such as Naver Cloud's FaceSign, by processing 6,144 biometric samples in 0.74 seconds using a 128-dimensional feature vector.

翻译：本文提出Blind-Match，一种基于同态加密（HE）的新型生物特征识别系统，可实现高效且隐私保护的1:N匹配。Blind-Match引入了一种HE优化的余弦相似度计算方法，其核心思想是将特征向量分割为较小部分进行处理，而非一次性计算整个向量。通过优化这些部分的数量，Blind-Match在利用HE确保数据隐私的同时，最小化了执行时间。与现有先进方法相比，Blind-Match在多种生物特征数据集上均表现出优越性能。在LFW人脸数据集上，Blind-Match使用128维特征向量实现了99.63%的Rank-1准确率，证明了其在人脸识别任务中的鲁棒性。在指纹识别方面，即使采用紧凑的16维特征向量，Blind-Match在PolyU数据集上仍取得了99.55%的Rank-1准确率，显著优于当前先进方法Blind-Touch（仅59.17%）。此外，Blind-Match在大规模生物特征识别场景中展现了实用效率，例如在Naver Cloud的FaceSign系统中，使用128维特征向量仅需0.74秒即可处理6,144个生物特征样本。