基于激励相容的权益分散共识算法

田有亮; 袁延森; 高鸿峰; 杨旸; 熊金波

doi:10.11959/j.issn.1000-436x.2022232

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基于激励相容的权益分散共识算法

学术论文 | 更新时间：2024-06-05

- 基于激励相容的权益分散共识算法
- Equity decentralized consensus algorithm based on incentive compatibility
- 通信学报 2022年43卷第12期页码：101-112
- 作者机构：
  
  1. 贵州大学公共大数据国家重点实验室，贵州贵阳 550025
  2. 贵州大学计算机科学与技术学院，贵州贵阳 550025
  3. 贵州大学密码学与数据安全研究所，贵州贵阳 550025
  4. 贵州省密码学与区块链技术特色重点实验室，贵州贵阳 550025
  5. 新加坡管理大学计算机与信息系统学院，新加坡 188065
  6. 福建师范大学计算机与网络空间安全学院，福建福州 350117
- 作者简介：
  
  [ "田有亮（1982– ），男，贵州盘县人，博士，贵州大学教授、博士生导师，主要研究方向为算法博弈论、密码学与安全协议、大数据安全与隐私保护等" ]
  [ "袁延森（1998– ），男，河南南阳人，贵州大学硕士生，主要研究方向为区块链技术、共识算法等" ]
  [ "高鸿峰（1975- ），男，贵州遵义人，贵州大学副教授、硕士生导师，主要研究方向为网络与信息安全" ]
  [ "杨旸（1984- ），女，湖北随州人，新加坡管理大学在站博士后，福州大学教授、博士生导师，主要研究方向为区块链、密文搜索、大数据安全等" ]
  [ "熊金波（1981- ），男，湖南益阳人，博士，福建师范大学教授、博士生导师，主要研究方向为大数据安全与隐私保护、区块链技术、安全深度学习" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2021YFB3101100);国家自然科学基金资助项目(U1836205);贵州省高层次创新型人才基金资助项目([2020]6008);贵阳市科技计划基金资助项目([2021]1-5);贵阳市科技计划基金资助项目([2022]2-4);贵州省科技计划基金资助项目([2020]5017);贵州省科技计划基金资助项目([2022]065)
- DOI：10.11959/j.issn.1000-436x.2022232
  中图分类号： TP302
- 网络出版日期：2022-12，
  
  纸质出版日期：2022-12-25
- 稿件说明：
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田有亮, 袁延森, 高鸿峰, 等. 基于激励相容的权益分散共识算法[J]. 通信学报, 2022,43(12):101-112.

Youliang TIAN, Yansen YUAN, Hongfeng GAO, et al. Equity decentralized consensus algorithm based on incentive compatibility[J]. Journal on communications, 2022, 43(12): 101-112.
田有亮, 袁延森, 高鸿峰, 等. 基于激励相容的权益分散共识算法[J]. 通信学报, 2022,43(12):101-112. DOI： 10.11959/j.issn.1000-436x.2022232.

Youliang TIAN, Yansen YUAN, Hongfeng GAO, et al. Equity decentralized consensus algorithm based on incentive compatibility[J]. Journal on communications, 2022, 43(12): 101-112. DOI： 10.11959/j.issn.1000-436x.2022232.

摘要

PoW共识算法被证明是激励不相容的，存在高奖励差异下的算力中心化和极端情况下的分叉收敛速度较慢等问题。基于此，提出了一种基于激励相容的 SSPoW 共识算法。通过引入局部解来计算区块链的聚合算力，利用算力的显性量化加快分叉收敛速度，从而满足区块链的一致性。通过改进奖励方案实现激励相容，减少因高奖励差异导致的算力中心化问题。仿真结果证明，所提算法能有效削减奖励差异，并且效率高于传统 PoW 共识算法，对提高系统安全性和共识效率有积极意义。

Abstract

The PoW consensus algorithm has been proved to be incentive incompatible

existing computing centralization under high reward differences and slow convergence of forks in extreme cases.Based on this

an incentive-compatiblebased consensus algorithm SSPoW was proposed.By introducing local solutions to calculate the computing power aggregated on the block chain

the explicit quantification of computing power was used to speed up the convergence of the fork

thus satisfying the consistency of the blockchain.Incentive compatibility was achieved by improving the reward scheme

which reduced the problem of computing centralization caused by high reward differences.Simulation results prove that the proposed algorithm could effectively reduce the reward differences and is more efficient than the traditional PoW consensus algorithm

which has positive implications for improving system security and consensus efficiency.

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references

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