数据要素市场研究综述：价值、定价与交易

杨明; 冯宏霖; 王鑫; 霍吉东; 焦绪国; 张恒

doi:10.11959/j.issn.2096-109x.2024036

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数据要素市场研究综述：价值、定价与交易

综述 | 更新时间：2024-08-14

- 数据要素市场研究综述：价值、定价与交易
- Survey of data factor market： value， pricing， and trading
- 网络与信息安全学报 2024年10卷第3期页码：1-19
- 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.山东省计算机网络重点实验室，山东省基础科学研究中心（计算机科学），山东济南 250014
  3.青岛理工大学信息与控制工程学院，山东青岛 266520
  4.浙江大学工业控制技术全国重点实验室，浙江杭州 310027
  5.江苏海洋大学计算机工程学院，江苏连云港 222005
- 作者简介：
  
  [ "杨明（1981- ），男，山东东营人，博士，齐鲁工业大学（山东省科学院）研究员、博士生导师，主要研究方向为密码学、云安全、大数据安全、物联网安全。" ]
  [ "冯宏霖（1997- ），男，山东淄博人，齐鲁工业大学（山东省科学院）研究生，主要研究方向为数据交易、数据定价。" ]
  [ "王鑫（1992- ），男，山东邹平人，博士，齐鲁工业大学（山东省科学院）副研究员、硕士生导师，主要研究方向为人工智能安全、数据隐私保护、工业物联网。" ]
  [ "霍吉东（1986- ），男，山东德州人，齐鲁工业大学（山东省科学院）助理研究员，主要研究方向为并行计算、大数据智能处理。" ]
  [ "焦绪国（1988- ），男，山东潍坊人，博士，青岛理工大学副教授、硕士生导师，主要研究方向为能源系统与智能控制。" ]
  [ "张恒（1981- ），男，江苏连云港人，博士，江苏海洋大学教授、博士生导师，主要研究方向为系统安全与控制和人工智能算法安全。" ]
- 基金信息：
  
  国家自然科学基金(62203249);山东省自然科学基金(ZR2021QF115);泰山学者计划(tsqn202211203);山东省高等学校青创科技支持计划(2022KJ290);山东省科技型中小企业创新能力提升项目(2023TSGC0108);济南市新高校20条项目(202228077)
- DOI：10.11959/j.issn.2096-109x.2024036
  中图分类号： TP391
- 收稿：2024-01-18，
  
  修回：2024-03-19，
  
  纸质出版：2024-06-25
- 稿件说明：
移动端阅览
杨明, 冯宏霖, 王鑫, 等. 数据要素市场研究综述:价值、定价与交易[J]. 网络与信息安全学报, 2024, 10(3): 1-19.

YANG M, FENG H L, WANG X, et al. Survey of data factor market:value, pricing, and trading[J]. Chinese Journal of Network and Information Security, 2024, 10(3): 1-19.
杨明, 冯宏霖, 王鑫, 等. 数据要素市场研究综述:价值、定价与交易[J]. 网络与信息安全学报, 2024, 10(3): 1-19. DOI： 10.11959/j.issn.2096-109x.2024036.

YANG M, FENG H L, WANG X, et al. Survey of data factor market:value, pricing, and trading[J]. Chinese Journal of Network and Information Security, 2024, 10(3): 1-19. DOI： 10.11959/j.issn.2096-109x.2024036.

摘要

随着算力网络的兴起，以算力为中心的新型基础设施成为推动信息经济发展的关键引擎。数据是数字经济时代重要的生产要素，是构建新发展格局的重要支撑。数据要素市场作为连接数据和算力网络的纽带显得尤为关键，其稳健运行直接关系到算力网络的可持续发展，为信息经济体系提供了关键支撑。因此，以数据产品为起点，介绍了其在数据要素市场中所涉及的流程和这些部分之间的内在联系。之后，深入研究了数据要素市场的3个关键方面的现有工作，即数据价值、数据定价和数据交易。数据价值评估方面，针对数据相对价值评估，介绍了指标体系、指标权重和价值指数等关键环节的相关工作，针对数据资产价值评估，介绍了成本法、收益法、市场法等传统方法和基于机器学习的新兴方法。数据定价模型方面，将现有方法划分为数据集产品定价和数据服务产品定价两大类，总结了7种定价方式，并进一步分析了各个方法的优缺点及适用场景。数据交易系统方面，从系统架构的角度分析了集中式和分布式两种架构在数据交易市场中的应用，之后介绍了场内交易和场外交易两种交易模式的现状及前景。

Abstract

With the rise of computing power networks

a novel infrastructure centered around computational power has emerged as a key driver in the development of the information economy. Data

recognized as a crucial production factor in the digital era

serves as a vital pillar for constructing new development paradigms. The data factor market

acting as the link between data and computational power networks

is particularly critical. Its robust operation directly affects the sustainable development of computational power networks and is provided with crucial support for the information economy system. Therefore

the data products were treated as the focal point

while the processes involved in the data factor market and the inherent connections among these components were introduced. Subsequently

the existing work on three key aspects of the data factor market was studied

including data value

data pricing

and data transactions. Regarding data value assessment

key aspects such as relative data value evaluation were discussed

introducing indicator systems

indicator weights

and value indices. For data asset value assessment

traditional methods such as the cost method

income method

and market method were discussed

as well as emerging methods based on machine learning. In terms of data pricing models

existing methods were categorized into two main types: pricing for dataset products and pricing for data service products. Seven pricing methods were summarized

and the advantages

disadvantages

and applicable scenarios of each method were further analyzed. Regarding data transaction systems

the applications of centralized and distributed architectures in the data trading market were analyzed from the perspective of system architecture. The current status and prospects of both on-exchange and over-the-counter trading modes were then introduced.

关键词

Keywords

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