Consensus Optimization Algorithm for Distributed Intelligent Medical Diagnostic Collaborative Systems Based on Verifiable Random Functions and Reputation Mechanisms

With the deep integration of distributed network technology and intelligent medical care, how to achieve efficient collaboration under the premise of safeguarding data security and system efficiency has become an important challenge for intelligent medical diagnosis systems. The traditional practical Byzantine fault tolerance (PBFT) algorithm has difficulty meeting the demands of large-scale distributed medical scenarios due to high communication overhead and poor scalability. In addition, the existing improvement schemes are still deficient in dynamic node management and complex attack defence. To this end, this paper proposes the VS-PBFT consensus algorithm, which fuses a verifiable random function (VRF) and reputation mechanism, and designs a distributed intelligent medical diagnosis collaboration system based on this algorithm. Firstly, we introduce the VRF technique to achieve random and unpredictable selection of master nodes, which reduces the risk of fixed verification nodes being attacked. Secondly, we construct a dynamic reputation evaluation model to quantitatively score the nodes’ historical behaviors and then adjust their participation priority in the consensus process, thus reducing malicious node interference and redundant communication overhead. In the application of an intelligent medical diagnosis collaboration system, the VS-PBFT algorithm effectively improves the security and efficiency of diagnostic data sharing while safeguarding patient privacy. The experimental results show that in a 40-node network environment, the transaction throughput of VS-PBFT is 21.05% higher than that of PBFT, the delay is reduced by 33.62%, the communication overhead is reduced by 8.63%, and the average number of message copies is reduced by about 7.90%, which demonstrates stronger consensus efficiency and anti-attack capability, providing the smart medical diagnosis collaboration system with the first VS-PBFT algorithm-based technical support.