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Research on auction-based pricing mechanisms for personal health data under privacy heterogeneity

Author

Listed:
  • Shen, Rui
  • Chen, Yingxi
  • Xiao, Fanghao
  • Peng, Yuan

Abstract

In the process of trading and circulation, personal health data faces the contradiction between privacy leakage and value appreciation, necessitating urgent mechanism design to optimize and eliminate the heterogeneity in privacy value perception between the supply and demand sides before transactions. This study aims to maximize the revenue of the trading system by allocating data trading tasks. By embedding privacy preference parameters and introducing a privacy perception coefficient, it seeks to coordinate the conflicting interests and heterogeneity between buyers and sellers.Additionally, this study devises a Nesterov accelerated gradient distributed iterative algorithm to adjust privacy protection intensity and transaction prices, aiming to achieve data supply-demand matching, privacy cost balance, and social welfare maximization. The findings reveal that the Nesterov algorithm converges globally, ensuring its theoretical robustness. Its fast-converging optimization provides an effective theoretical tool and practical reference for solving distributed computing issues in complex market environments. Moreover, the bilateral auction pricing mechanism enables efficient data matching, personalized privacy cost compensation, and maximization of overall social welfare.

Suggested Citation

  • Shen, Rui & Chen, Yingxi & Xiao, Fanghao & Peng, Yuan, 2026. "Research on auction-based pricing mechanisms for personal health data under privacy heterogeneity," Economic Analysis and Policy, Elsevier, vol. 90(C), pages 1279-1296.
    • Handle: RePEc:eee:ecanpo:v:90:y:2026:i:c:p:1279-1296
    DOI: 10.1016/j.eap.2026.01.046
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