基于信任度评估的分布式算力交易方案

王英晓春; 王连海; 徐淑奖; 张淑慧; 刘天瑞

doi:10.11959/j.issn.2096-109x.2025008

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基于信任度评估的分布式算力交易方案

学术论文 | 更新时间：2025-03-20

- 基于信任度评估的分布式算力交易方案
- Distributed computing power trading scheme based on trustworthiness assessment
- 网络与信息安全学报 2025年11卷第1期页码：79-91
- 作者机构：
  
  1.齐鲁工业大学（山东省科学院），山东省计算中心（国家超级计算济南中心），算力互联网与信息安全教育部重点实验室，山东济南 250014
  2.山东省工业网络和信息系统安全重点实验室，山东省基础科学研究中心（计算机科学），山东济南 250014
- 作者简介：
  
  [ "王英晓春（1998- ），女，山东青岛人，齐鲁工业大学（山东省科学院）硕士生，主要研究方向为数据安全、区块链应用。" ]
  [ "王连海（1969- ），男，山东潍坊人，齐鲁工业大学（山东省科学院）/山东省计算中心（国家超级计算济南中心）研究员，主要研究方向为数据安全、区块链、计算机取证。" ]
  [ "徐淑奖（1979- ），男，山东临沂人，齐鲁工业大学（山东省科学院）/山东省计算中心（国家超级计算济南中心）研究员，主要研究方向为数据安全、区块链。" ]
  [ "张淑慧（1981- ），女，山东济宁人，齐鲁工业大学（山东省科学院）/山东省计算中心（国家超级计算济南中心）研究员，主要研究方向为数据安全、区块链、计算机取证。" ]
  [ "刘天瑞（1995- ），男，山东济南人，齐鲁工业大学（山东省科学院）硕士生，主要研究方向为区块链技术、联邦学习。" ]
- 基金信息：
  
  国家自然科学基金(62102209);国家重点研发计划(2023YFC3304903);济南市“新高校20条”资助项目(202228017);泰山学者计划(tsqn202312231);山东省自然科学基金(ZR2024MF104;ZR2023QF129);齐鲁工业大学（山东省科学院）科教产融合试点工程重大创新类项目(2024ZDZX08);齐鲁工业大学（山东省科学院）人才项目(2023RCKY144)
- DOI：10.11959/j.issn.2096-109x.2025008
  中图分类号： TP393
- 收稿：2024-03-28，
  
  修回：2024-09-26，
  
  纸质出版：2025-02-25
- 稿件说明：
移动端阅览
王英晓春, 王连海, 徐淑奖, 等. 基于信任度评估的分布式算力交易方案[J]. 网络与信息安全学报, 2025, 11(1): 79-91.

WANG Y X C, WANG L H, XU S J, et al. Distributed computing power trading scheme based on trustworthiness assessment[J]. Chinese Journal of Network and Information Security, 2025, 11(1): 79-91.
王英晓春, 王连海, 徐淑奖, 等. 基于信任度评估的分布式算力交易方案[J]. 网络与信息安全学报, 2025, 11(1): 79-91. DOI： 10.11959/j.issn.2096-109x.2025008.

WANG Y X C, WANG L H, XU S J, et al. Distributed computing power trading scheme based on trustworthiness assessment[J]. Chinese Journal of Network and Information Security, 2025, 11(1): 79-91. DOI： 10.11959/j.issn.2096-109x.2025008.

摘要

算力网络主要通过连接和整合分布式的异构计算节点，实现网络和计算资源的优化及高效利用。在算力网络中，节点因自身资源受限，通常需要以交易形式请求其他节点的计算资源来完成计算任务，引入信任度评估机制可以有效解决泛在异构节点所引起的节点可信度不高问题。然而，现有的算力交易方案虽然将信誉作为选择交易方的重要依据，但通常会选择信誉最高的节点，导致新加入节点在算力交易中无法享有公平参与交易的机会，且由于交易环境不可信，节点很难评估交易反馈的可信度。为了解决上述问题，提出了基于信任度评估的分布式算力交易方案，通过智能合约对满足算力需求的计算节点实施自动化的信任度评估，生成信任度降序列表，基于随机选择算法从信任度高于一定阈值的节点中选定交易方，以保证资源选择的随机性，有效防止合谋攻击。同时，利用适应性函数动态调整直接信任和间接信任的权重，以跟踪计算节点的行为变化。此外，借助区块链技术，设计了一种衡量反馈可靠性的方法，以减少因不可信节点提交恶意评分所造成的评估结果偏差。最后，通过实验证明了所提方案在识别恶意评分和降低恶意行为影响方面的有效性。

Abstract

The computing power network was primarily designed to optimize and efficiently utilize network and computing resources by connecting and integrating distributed heterogeneous computing nodes. Within this network

nodes frequently required computing resources from other nodes through trading mechanisms due to their limited local resources. The introduction of a trust evaluation mechanism effectively addressed the issue of low trustworthiness among ubiquitous heterogeneous nodes. However

existing computing power trading schemes

which relied on reputation as a key criterion for selecting trading partners

typically favored nodes with the highest reputation. This approach resulted in newly joined nodes being unable to participate fairly in computing power trading

and the untrustworthy trading environment made it difficult for nodes to assess the credibility of trading feedback. To address these challenges

a distributed computing power trading scheme based on trust evaluation was proposed. In this scheme

the trust evaluation of computing nodes meeting the computing power requirements was automated through smart contracts

generating a trust list sorted in descending order. A random selection algorithm was utilized to choose trading partners from nodes with trust levels above a predefined threshold

ensuring randomness in resource selection and effectively mitigating collusion attacks. Additionally

an adaptive function was employed to dynamically adjust the weights of direct and indirect trust

enabling the tracking of behavioral changes in computing nodes. Furthermore

a method for measuring feedback reliability was designed using blockchain technology to reduce the bias in evaluation results caused by untrustworthy nodes submitting malicious ratings. Finally

experimental results demonstrated that the proposed scheme effectively identified malicious ratings and reduced the impact of malicious behaviors. The scheme is shown to ensure fair participation in trading for newly joined nodes while enhancing the reliability of trading feedback.

关键词

Keywords

references

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