数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (5): 63-73.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.05.006

doi: 10.11871/jfdc.issn.2096-742X.2023.05.006

• 专刊:数据要素安全高效流通的关键技术 • 上一篇    下一篇

基于数据纯度的模型博弈定价方法

江东1(),张小伟1,袁野2,*()   

  1. 1.东北大学，计算机科学与工程学院，辽宁 沈阳 110169
    2.北京理工大学，计算机学院，北京 100081
  • 收稿日期:2023-04-28 出版日期:2023-10-20 发布日期:2023-10-31
  • 通讯作者: 袁野（E-mail: yuanye@mail.neu.edu.cn）
  • 作者简介:江东，东北大学，博士研究生，CCF学生会员，主要研究领域为大图数据分析，数据定价。
    本文承担的工作为算法设计，文章撰写，实验方案规划。
    JIANG Dong is a Ph.D. candidate at Northeastern University and a CCF student member. His main research interests include graph data analysis and data pricing.
    In this paper, he is responsible for the design of the algorithms, paper writing, and the experiment plan development.
    E-mail: jiangdongcs@126.com|袁野，北京理工大学，博士，教授，博士生导师，CCF高级会员，主要研究领域为大数据管理，数据库理论与系统。
    本文承担的工作为：研究指导。
    YUAN Ye is a professor at Beijing Institute of Technology and a CCF senior member. His main research interests include big data management, database theory and systems.
    In this paper, he is responsible for research guidance.
    E-mail: yuanye@mail.neu.edu.cn
  • 基金资助:
    国家自然科学基金重点项目“应用驱动的大图数据建模理论与算法研究”(61932004);国家自然科学基金重点项目“大图数据管理与分析的基础理论与关键技术研究”(61732003);国家自然科学基金面上项目“演化异质信息网络集成关键技术研究”(62072087);国家自然科学基金联合基金项目“大规模数据驱动的机器学习理论与方法”(U2001211)

A Model Game Pricing Method Based on Data Purity

JIANG Dong1(),ZHANG Xiaowei1,YUAN Ye2,*()   

  1. 1. School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110169, China
    2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
  • Received:2023-04-28 Online:2023-10-20 Published:2023-10-31

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摘要：

【目的】 在大数据时代，数据流通和共享已经是大势所趋。因此数据定价与交易方法近来广受关注。作为数据定价的重要组成部分，模型定价是数据定价研究的重中之重。在模型定价中，首先需要解决数据拥有者的补偿问题和参与人的收益最大化问题。其次，为了使数据拥有者放心地参与到数据交易中，还要回应其隐私需求。同时，作为模型精度直接影响因素的数据质量也是研究重点。【方法】 因此本文在考虑用户隐私需求和数据质量的基础上，提出了基于数据纯度的模型博弈定价方法，在数据收购端以数据质量和噪声多少即隐私保护水平作为补偿依据，在模型出售端以博弈论为定价方法。【结果】 本文提出的方法既能给予数据拥有者公平便利的补偿，又能使数据平台和模型购买者收益最大化。【局限】 但是对于复杂交易环境下的博弈过程仍需相应改进。【结论】 通过实验证明了方法的有效性，为模型定价和数据市场的发展提供了新思路。

关键词: 数据质量, 博弈论, 数据市场, 模型定价, 隐私保护

Abstract:

[Objective] In the big data era, data circulation and sharing has become the trend. Hence data pricing and trading methods have received a lot of attention. As an important part of data pricing, model pricing is the top priority in data pricing research. In model pricing, it is necessary to solve the problem of compensation for data owners and maximization of benefits for all participants. Furthermore, to make sure that data owners participate in data trading with confidence, and their privacy concerns must be satisfied. At the same time, data quality, which has a direct influence on the accuracy of learning models, is also the focus of this research. [Methods] In this paper, we propose a model game pricing method based on data purity, using data quality and noise as the compensation basis on the data acquisition side and game theory on the model selling side. [Results] The proposed method can give fair and convenient compensation to data owners and maximize the revenue of data platforms and model buyers. [Limitations] However, the gaming process in complex trading environments still needs to be improved accordingly. [Conclusions] The effectiveness of the methods is demonstrated by experiments and new ideas are provided for the development of model pricing and data marketplaces.

Key words: data quality, game theory, data marketplace, model pricing, privacy protection