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A Practical Byzantine Fault Tolerance Algorithms Based on Randomized Mean Clustering, Trust and Credibility

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Data Mining and Big Data (DMBD 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2017))

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Abstract

The Practical Byzantine Fault Tolerance (PBFT) consensus protocol, while robust, faces efficiency challenges with the escalating number of network nodes. Various enhancement algorithms employ grouping strategies to bolster performance in large-scale networks. However, as nodes consolidate into smaller clusters, their capacity to accommodate Byzantine nodes diminishes markedly, prompting the need for a systematic approach to distributing Byzantine nodes evenly across consensus regions. In order to address the above issues, this paper proposes a Practical Byzantine fault tolerant algorithm based on Randomized Mean Clustering, Trust, and Credibility (CTPBFT). Nodes possess two key attributes: trust and credibility. Trust determines the priority of node cluster allocation, while credibility evaluations ensure cluster uniformity. A customized random equalization algorithm assigns nodes within the federated chain to diverse clusters. Post-partitioning, the consensus process is streamlined, with each cluster autonomously conducting consensus activities. A well-designed reward function incentivizes nodes to execute the consensus protocol, aiding the system’s smooth and efficient operation.

Supported by the National Natural Science Foundation of China (62072392,61972360), the Major Innovation Project of Science and Technology of Shandong Province (2019522Y020131), the Natural Science Foundation of Shandong Province (ZR2020QF113) and the Yantai Key Laboratory: intelligent technology of high-end marine engineering equipment.

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Authors and Affiliations

  1. Yantai University, College of Computer and Control Engineering, Yantai, China

    Haonan Zhai & Xiangrong Tong

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  1. Haonan Zhai
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  2. Xiangrong Tong
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Correspondence to Xiangrong Tong .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Techn, Shenzhen, China

    Yuhui Shi

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Zhai, H., Tong, X. (2024). A Practical Byzantine Fault Tolerance Algorithms Based on Randomized Mean Clustering, Trust and Credibility. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2023. Communications in Computer and Information Science, vol 2017. Springer, Singapore. https://doi.org/10.1007/978-981-97-0837-6_5

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  • DOI: https://doi.org/10.1007/978-981-97-0837-6_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0836-9

  • Online ISBN: 978-981-97-0837-6

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