Secure Two-Party Euclidean Distance Algorithm Based on Quantum Entanglement
Shuoying Zhang, Zhongzhu Liu, Zhihong Wu, Sufang Yin, Xingbang Deng, Jianwei Zhang · 2025
With the advent of the big data era, crossorganizational collaborative data computing faces challenges in privacy protection. Traditional secure multi-party computation (MPC) protocols suffer from security insufficiency under the threat of quantum computing against classical encryption algorithms. To address this issue, this paper proposes a secure quantum protocol based on mutually unbiased bases (MUBs) in dlevel quantum systems. By leveraging quantum entanglement and quantum measurement techniques, the protocol accurately calculates the Euclidean distance between participants while ensuring privacy protection during the computation. Experimental results demonstrate that the protocol not only enhances computational efficiency but also effectively resists external attacks, participant attacks, and semi-honest third-party attacks, providing a new approach for the application of quantumsafe computing in privacy protection.