基于多目标效用优化的分布式数据交易算法

黄小红; 张勇; 闪德胜; 钱叶魁; 韩璐; 李丹丹; 丛群

doi:10.11959/j.issn.1000-436x.2021034

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基于多目标效用优化的分布式数据交易算法

学术论文 | 更新时间：2024-06-05

- 基于多目标效用优化的分布式数据交易算法
- Distributed data trading algorithm based on multi-objective utility optimization
- 通信学报 2021年42卷第2期页码：52-63
- 作者机构：
  
  1. 北京邮电大学计算机学院（国家示范性软件学院），北京 100876
  2. 中国人民解放军32147部队，陕西宝鸡 721000
  3. 陆军炮兵防空兵学院郑州校区，河南郑州 450052
  4. 北京网瑞达科技有限公司技术研发部，北京 100876
- 作者简介：
  
  [ "黄小红（1979- ），女，广州佛山人，博士，北京邮电大学教授，主要研究方向为计算机网络应用、下一代互联网和网络安全等。" ]
  [ "张勇（1990- ），男，河北衡水人，北京邮电大学博士生，主要研究方向为区块链、大数据交易和数据隐私保护等。" ]
  [ "闪德胜（1963- ），男，河南孟县人，中国人民解放军32147部队高级工程师，主要研究方向为网络测量、网络安全等。" ]
  [ "钱叶魁（1980- ），男，安徽枞阳人，博士，陆军炮兵防空兵学院教授、硕士生导师，主要研究方向为网络测量、网络安全等。" ]
  [ "韩璐（1991- ），女，蒙古族，内蒙古赤峰人，北京邮电大学博士生，主要研究方向为安全多方计算、联邦学习等。" ]
  [ "李丹丹（1987- ），女，河南平顶山人，博士，北京邮电大学讲师，主要研究方向为网络安全、密码学等。" ]
  [ "丛群（1980- ），男，辽宁盘锦人，北京网瑞达科技有限公司高级工程师，主要研究方向为网络管理。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2020YFE0200500);北京邮电大学优秀博士生创新基金资助项目(CX2019212)
- DOI：10.11959/j.issn.1000-436x.2021034
  中图分类号： TP399
- 网络出版日期：2021-02，
  
  纸质出版日期：2021-02-25
- 稿件说明：
移动端阅览
黄小红, 张勇, 闪德胜, 等. 基于多目标效用优化的分布式数据交易算法[J]. 通信学报, 2021,42(2):52-63.

Xiaohong HUANG, Yong ZHANG, Desheng SHAN, et al. Distributed data trading algorithm based on multi-objective utility optimization[J]. Journal on communications, 2021, 42(2): 52-63.
黄小红, 张勇, 闪德胜, 等. 基于多目标效用优化的分布式数据交易算法[J]. 通信学报, 2021,42(2):52-63. DOI： 10.11959/j.issn.1000-436x.2021034.

Xiaohong HUANG, Yong ZHANG, Desheng SHAN, et al. Distributed data trading algorithm based on multi-objective utility optimization[J]. Journal on communications, 2021, 42(2): 52-63. DOI： 10.11959/j.issn.1000-436x.2021034.

摘要

传统的集中式数据交易模式不能很好地适用于当前万物互联、数据实时产生的智能时代，为了使产生的数据发挥更大的价值，设计一种有效的数据交易框架至关重要。为此，提出了一种基于联盟区块链的分布式数据交易框架，在不依赖第三方的情况下实现了P2P的数据交易。针对已有数据交易模型仅考虑数据本身的因素，而忽略用户任务相关因素的问题，基于数据质量、数据属性、属性的相关性、消费者竞争等多维因素构建了双层多目标优化模型，以优化数据提供者（DP）和数据消费者（DC）的效用。为求解上述模型，提出了一种改进的多目标遗传算法——协作式NSGAII，通过DP、DC以及数据聚合器（AG）的协作进行计算。仿真结果表明，所提算法在DP和DC的效用方面取得了更好的性能，实现了更有效的数据交易。

Abstract

The traditional centralized data trading models are not well applicable to the current intelligent era where everything is interconnected and real-time data is generated

and in order to maximize the use of collected data

it is essential to design an effective data trading framework.Therefore

a distributed data trading framework based on consortium blockchain was proposed

which realized P2P data trading without relying on a third party.Aiming at the problem that existing data trading models only consider the factors of the data itself and ignore the factors related to user tasks

a bi-level multi-objective optimization model was constructed based on multi-dimensional factors

such as data quality

data attributes

attribute relevance and consumer competition

to optimize the utilities of data provider (DP) and data consumer (DC).To solve the above model

an improved multi-objective genetic algorithm-collaborative NSGAII was proposed

calculated by the cooperation of DP

DC and data aggregator (AG).The simulation results show that the collaborative NSGAII achieves better performance in terms of the utilities of DP and DC

thus realizing more effective data trading.

关键词

Keywords

references

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