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Practical Byzantine fault tolerance consensus based on comprehensive reputation

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Abstract

Consensus protocol is challenging due to the poor node reliability, low efficiency and decentralization. A comprehensive reputation based Practical Byzantine Fault Tolerance consensus method (CRPBFT) has been proposed. Comprehensive reputation model has been developed to evaluate the credibility of each node from service behavior and consensus process at first. The nodes with higher reputation are selected to participate in the consensus process, which helps to reduce the probability of consensus failure caused by the existence of malicious nodes. A consensus communication structure is optimized by replacing the whole network broadcast structure in the commit phase with a star one. It can be applied to degrade the network communication overhead and improve consensus efficiency. A rotation mechanism for replacing the consensus nodes regularly has been proposed to increase the degree of decentralization and enhance the robustness and dynamic of the consensus network. Some experimental results demonstrate that the developed method has excellent performance by comparisons with some state-of-the-arts.

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References

  1. Lu Y, Huang X, Dai Y (2019) Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Trans Industr Inf 16(6):4177–4186

    Article  Google Scholar 

  2. Huang J, He D, Obaidat MS (2021) The application of the blockchain technology in voting systems: A review. ACM Comput Surv 54(3):1–28

    Article  Google Scholar 

  3. Li X, Jiang P, Chen T (2020) A survey on the security of blockchain systems. Futur Gener Comput Syst 107(1):841–853

    Article  Google Scholar 

  4. Buterin V (2014) A next-generation smart contract and decentralized application platform. White Paper 3(37):1–36

    Google Scholar 

  5. Rezaeibagha F, Mu Y (2019) Efficient micropayment of cryptocurrency from blockchains. Comput J 62(4):507–517

    Article  MathSciNet  Google Scholar 

  6. Harish AR, Liu XL, Zhong RY (2021) Log-flock: a blockchain-enabled platform for digital asset valuation and risk assessment in E-commerce logistics financing. Comput Ind Eng 151(1):1–13

    Google Scholar 

  7. Hakak S, Khan WZ, Gilkar GA (2020) Securing smart cities through blockchain technology: architecture, requirements, and challenges. IEEE Network 34(1):8–14

    Article  Google Scholar 

  8. Tian H, He J, Ding Y (2019) Medical data management on blockchain with privacy. J Med Syst 43(2):1–6

    Article  Google Scholar 

  9. Musamih A, Salah K, Jayaraman R (2021) A blockchain-based approach for drug traceability in healthcare supply chain. IEEE Access 9(1):9728–9743

    Article  Google Scholar 

  10. Gramoli V (2020) From blockchain consensus back to Byzantine consensus. Futur Gener Comput Syst 107(2):760–769

    Article  Google Scholar 

  11. Zheng Z, Xie S, Dai HN (2018) Blockchain challenges and opportunities: A survey. Int J Web Grid Serv 14(4):352–375

    Article  Google Scholar 

  12. Castro M, Liskov B (2002) Practical Byzantine fault tolerance and proactive recovery. ACM Trans Comput Syst 20(4):398–461

    Article  Google Scholar 

  13. Behl J, Distler T, Kapitza R (2014) Scalable BFT for multi-cores: actor-based decomposition and consensus-oriented parallelization. Hot Topics in System Dependability, 245–276

  14. Miller A, Xia Y, Croman K (2016) The honey badger of BFT protocols. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 31–42

  15. Garcia M, Neves N, Bessani A (2016) SieveQ: A layered BFT protection system for critical services. IEEE Trans Dependable Secure Comput 15(3):511–525

    Article  Google Scholar 

  16. Yin M, Malkhi D, Reiter MK (2019) HotStuff: BFT consensus with linearity and responsiveness. Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, 347–356

  17. Gueta GG, Abraham I, Grossman S (2018) Sbft: A scalable decentralized trust infrastructure for blockchains. Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 1804

  18. Gan Y (2021) A fully adaptively secure threshold signature scheme based on dual-form signatures technology. Security and Communication Networks 2021(5):234–267

    Google Scholar 

  19. Li W, Feng C, Zhang L (2020) A scalable multi-layer PBFT consensus for blockchain. IEEE Trans Parallel Distrib Syst 32(5):1146–1160

    Article  Google Scholar 

  20. Li Y, Qiao L, Lv Z (2021) An optimized byzantine fault tolerance algorithm for consortium blockchain. Peer-to-Peer Networking and Applications 2021(1):1–14

    Google Scholar 

  21. Huang D, Ma X, Zhang S (2019) Performance analysis of the raft consensus algorithm for private blockchains. IEEE Trans Syst 50(1):172–181

    Google Scholar 

  22. Fan X (2018) Scalable practical byzantine fault tolerance with short-lived signature schemes. Proceedings of the 28th Annual International Conference on Computer Science and Software Engineering, 245–256

  23. Li Y, Wang Z, Fan J (2019) An extensible consensus algorithm based on PBFT. 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 17–23

  24. Zhan Y, Wang B, Lu R (2021) DRBFT: Delegated randomization Byzantine fault tolerance consensus protocol for blockchains. Inf Sci 559(1):8–21

    Article  MathSciNet  Google Scholar 

  25. Chander G, Deshpande P, Chakraborty S (2019) A fault resilient consensus protocol for large permissioned blockchain networks. 2019 IEEE International Conference on Blockchain and Cryptocurrency, 33–37

  26. Yang J, Jia Z, Su R (2022) Improved fault-tolerant consensus based on the PBFT algorithm. IEEE Access 10(1):30274–30283

    Article  Google Scholar 

  27. Li Y, Cheng J, Li H (2022) A survey of consensus mechanism based on reputation model. International Conference on Artificial Intelligence and Security, 208–221

  28. Gao S, Yu T, Zhu J (2019) T-PBFT: An EigenTrust-based practical Byzantine fault tolerance consensus algorithm. China Commun 16(12):111–123

    Article  Google Scholar 

  29. Sun Y, Xue R, Zhang R (2020) Rtchain: A reputation system with transaction and consensus incentives for e-commerce blockchain. ACM Trans Internet Technol 21(1):1–24

    Article  Google Scholar 

  30. Yuan X, Luo F, Haider MZ (2021) Efficient Byzantine consensus mechanism based on reputation in IoT blockchain. Wirel Commun Mob Comput 2021(1):1–14

    Google Scholar 

  31. Bugday A, Ozsoy A, Öztaner SM (2019) Creating consensus group using online learning based reputation in blockchain networks. Pervasive Mob Comput 59(1):101–126

    Google Scholar 

  32. Du M, Chen Q, Ma X (2020) MBFT: A new consensus algorithm for consortium blockchain. IEEE Access 8(1):87665–87675

    Article  Google Scholar 

  33. Wang Y, Cai S, Lin C (2019) Study of blockchains’s consensus mechanism based on credit. IEEE Access 7(1):10224–10231

    Article  Google Scholar 

  34. Zhang Z, Zhu D, Fan W (2020) Qpbft: Practical byzantine fault tolerance consensus algorithm based on quantified-role. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, 991–997

  35. Liu W, Zhang X, Feng W (2022) Optimization of PBFT algorithm based on QoS-aware trust service evaluation. Sensors 22(12):4590–4598

    Article  Google Scholar 

  36. Al-Masri E, Mahmoud QH (2007) Qos-based discovery and ranking of web services[C]//2007 16th international conference on computer communications and networks. IEEE, 529–534

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Acknowledgements

This work is supported in part by National Key R&D Program of China (Grant No. 2020YFC1523004)

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China

    Jiamou Qi & Yepeng Guan

  2. Key Laboratory of Advanced Display and System Application, Ministry of Education, Shanghai, 200072, China

    Yepeng Guan

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  1. Jiamou Qi
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  2. Yepeng Guan
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Correspondence to Yepeng Guan.

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Qi, J., Guan, Y. Practical Byzantine fault tolerance consensus based on comprehensive reputation. Peer-to-Peer Netw. Appl. 16, 420–430 (2023). https://doi.org/10.1007/s12083-022-01408-2

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