Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric Identification

TL;DR

Blind-Match introduces a homomorphic encryption-based biometric identification system that optimizes cosine similarity computation by dividing feature vectors, achieving high accuracy and efficiency in face and fingerprint recognition tasks.

Contribution

It presents a novel HE-optimized cosine similarity method for privacy-preserving 1:N biometric matching, significantly improving performance over existing approaches.

Findings

Achieves 99.63% accuracy on LFW face dataset.

Attains 99.55% accuracy on PolyU fingerprint dataset.

Processes 6,144 samples in 0.74 seconds.

Abstract

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.