Big Data Research ›› 2024, Vol. 10 ›› Issue (2): 54-67.doi: 10.11959/j.issn.2096-0271.2024025

• DATA ASSETIZATION TECHNOLOGY • Previous Articles    

Fair data pricing based on data quality

Siying CHEN, Dan ZHANG, Xiaoou DING, Hongzhi WANG   

  1. Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
  • Online:2024-03-01 Published:2024-03-01
  • Supported by:
    The National Key Research and Development Program of China(2021YFB3300502);The National Natural Science Foundation of China(62202126);The National Natural Science Foundation of China(62232005);China Postdoctoral Science Foundation(2022M720957);Heilongjiang Postdoctoral Financial Assistance(LBH-Z21137)

Abstract:

With the explosive growth of data, the digital economy, where data serves as a crucial element, continues to advance.In the context of data markets, establishing a fair and efficient pricing and trading system becomes paramount.Addressing fairness within data markets, this study introduces a data market model based on data quality.Firstly, aiming at user demands, a comprehensive pricing strategy based on data quality is formulated.Secondly, to mitigate malicious fraudulent behaviors from users, a market mechanism ensuring fair data transactions is designed.Lastly, building upon primary data transactions, data cleaning services related to data quality are discussed.A multi-user value allocation mechanism for cleaning is designed using principles from game theory.Experimental results demonstrate that constructing systems according to this model ensures both efficiency and fairness within data markets.

Key words: data market, data pricing, fairness

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