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A grouping and reputation‑based consensus method for consortium blockchain systems

algorithms-and-analysis-of-algorithms
data-mining-and-machine-learning
distributed-and-parallel-computing
security-and-privacy
blockchain

Abstract

Currently, consortium blockchain systems are widely applied in banking, finance, and other sectors. However, the Practical Byzantine Fault Tolerance (PBFT) algorithm used in these systems suffers from issues such as high communication costs, random selection of leaders, and poor adaptability. To address these issues, this paper proposes a grouping and reputation-based PBFT consensus method (LR-PBFT). The algorithm first divides nodes into groups using an improved K-medoids clustering algorithm, transforming node consensus into a two-stage process: intra-group and inter-group consensus. Simultaneously, a reputation value mechanism is introduced. By assigning reputation values to nodes, they are categorized into different types. During leader selection, the node with the highest reputation value is chosen as the leader, enhancing system security. Furthermore, during consensus, nodes with good reputation values are selected to participate, while those with excessively low reputation values are removed from the consensus group, ensuring the algorithm's dynamism and security. Experimental results demonstrate that compared to the traditional PBFT algorithm, the LR-PBFT algorithm achieves a 70% reduction in communication complexity, a 78% increase in average throughput, and a 45% decrease in average latency. Compared to K-PBFT, it achieves an 18% increase in average throughput and a 32% decrease in average latency. Compared to RD-PBFT, it achieves an 11% increase in average throughput and a 25.79% decrease in average latency.
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