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A practical byzantine fault tolerance improvement algorithm based on credit grouping-classification

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Abstract

Blockchain is a distributed system that maintains a public ledger using consensus protocols. The practical byzantine fault tolerance (PBFT) protocol is vital in consortium blockchains. However, there are still problems with system centralization, consensus inefficiency, high communication complexity, and other issues in PBFT and its improved algorithms. These restrict its widespread application in realistic environments. In this work, we propose a grouping-classification practical byzantine fault tolerance (GC-PBFT) improvement algorithm based on credit. First, we construct a credit model to dynamically evaluate the credit of nodes and rank them in descending order. Second, a grouping-classification model is built to divide nodes into three subgroups. Then, primary node, supervisory node, and consensus node are selected from these subgroups randomly, ensuring that the node roles are constantly updated to improve the security and fairness of the system. Additionally, the consensus process is simplified to improve the efficiency of the consensus and reduce communication complexity. Finally, experiments show that compared with PBFT and its improved algorithms, the GC-PBFT algorithm reduces communication complexity from \(O(N^2)\) to O(N). In addition, active and honest nodes can obtain higher credit, which lowers the possibility of malicious nodes being selected as consensus nodes, leading to higher consensus efficiency and better decentralization characteristics for the system.

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Data and materials availability

The raw/processed data required to reproduce these findings cannot be shared as the data also form part of an ongoing study. The data supporting this study’s results are available on request from the author via e-mail to: Yilin Li [13235055799@163.com].

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Funding

This work was supported by the National Social Science Fund of China (No.21XTQ015), the Natural Science Foundation of Fujian Province of China under Grant (Nos. 2023J01920, 2020J01905, 2020J01814), and the presidential research fund of Minnan Normal University (No. KJ18024).

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Author notes

  1. Amei Lan and Zhenjie Huang have contributed equally.

Authors and Affiliations

  1. School of Computer Science, Minnan Normal University, Xianqianzhi Street, Zhangzhou, 363000, Fujian, China

    Yilin Li, Hui Huang, Amei Lan & Zhenjie Huang

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  1. Yilin Li
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  2. Hui Huang
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Contributions

Yilin Li involved in conceptualization and writing the original draft. Hui Huang involved in methodology and writing—review and editing. Amei Lan involved in curating the software and conducting formal analysis. Zhengjie Huang involved in supervision.

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Correspondence to Hui Huang.

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Li, Y., Huang, H., Lan, A. et al. A practical byzantine fault tolerance improvement algorithm based on credit grouping-classification. J Supercomput 80, 20270–20301 (2024). https://doi.org/10.1007/s11227-024-06199-2

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