BloP: A Trusted Computing Scheme Integrating Blockchain and Privacy-Preserving Computation
Zhihao Zhang, Zhonghao Pan, Yang Feng, Qingni Shen · 2025
In today's era of rapid digitalization, industries with high data security demands increasingly rely on reliable systems. The requirements for data security and privacy protection in their operations have become more prominent. Although federated learning offers advantages in data privacy protection and collaborative modeling, it still faces privacy risks during model iteration and training interference. It has become an urgent challenge for industries with high data security requirements to build a sensitive information protection system to ensure security and efficiency of data processing. To address these challenges, we propose BloP, a trusted computing scheme that integrates blockchain with privacy-preserving computation. The scheme combines blockchain algorithms with various privacy-preserving computation technologies. BloP relies on trusted computing and measurement modules to maintain a set of trusted nodes, monitor trusted anomaly events, and establish a tamper-proof mechanism. In addition, BloP employs the PBFT consensus algorithm to accelerate the blockchain algorithm. BloP has been implemented and tested on 10 industry systems with high data security demands. During 17 months, the system detected 32,220 trusted anomaly events and 479 tamper-proof events. Furthermore, more than 90 % of these trusted anomaly events were caused by operational errors, while the rest were malicious attacks or unknown incidents.