Secure and Scalable Dynamic Blockchain Sharding via a Multi-Granularity Reputation Model

Abstract

Sharding technology divides the blockchain network into multiple parallel-processing subnetworks, achieving high throughput and scalability. However, it also faces challenges, including the risk of 51% attack caused by malicious node clustering and systemic load imbalances. While reputation mechanisms are widely employed to mitigate these risks, existing approaches remain constrained by unidimensional evaluations. Specifically, most studies focus on node behavior and assess node security metrics, neglecting node performance metrics and node heterogeneity. To address these challenges, this paper presents a multi-granularity reputation model to quantify the efficiency and reliability of nodes. This model accounts for the performance and security differences arising from node heterogeneity and behavioral dynamics. Based on this model, we further propose a neighborhood-constrained simulated annealing-based node partition algorithm, NCSA-NP, that achieves balanced security and performance across shards. Experimental results demonstrate that the proposed approach achieves significant improvements in throughput and latency compared to other baselines.