Privacy-Preserving Gait Authentication Scheme Based on Partial Euclidean Distance in Cloud Computing

With the rapid development of artificial intelligence and big data technologies, gait recognition has become a key method for identity authentication. As a unique biometric characteristic, gait is difficult to counterfeit and supports long-distance, non-contact authentication, making it ideal for security, surveillance, and health monitoring. However, traditional gait authentication faces privacy and efficiency challenges. This paper presents a privacy-preserving gait authentication scheme based on partial Euclidean distance calculation, and the scheme encrypts gait features using block-diagonal orthogonal matrices and random perturbation vectors, enabling efficient encrypted computation on the cloud. Experimental results demonstrate that the proposed scheme improves processing efficiency and matching accuracy while protecting privacy. For instance, on the CASIA-B dataset, it reduces computation time by approximately 30% without compromising accuracy.