Open Access

ARTICLE

A Fair and Trusted Trading Scheme for Medical Data Based on Smart Contracts

Xiaohui Yang, Kun Zhang^*

School of Cyber Security and Computer, Hebei University, Baoding, 071000, China

* Corresponding Author: Kun Zhang. Email:

Computers, Materials & Continua 2024, 78(2), 1843-1859. https://doi.org/10.32604/cmc.2023.047660

Received 13 November 2023; Accepted 15 December 2023; Issue published 27 February 2024

Abstract

Data is regarded as a valuable asset, and sharing data is a prerequisite for fully exploiting the value of data. However, the current medical data sharing scheme lacks a fair incentive mechanism, and the authenticity of data cannot be guaranteed, resulting in low enthusiasm of participants. A fair and trusted medical data trading scheme based on smart contracts is proposed, which aims to encourage participants to be honest and improve their enthusiasm for participation. The scheme uses zero-knowledge range proof for trusted verification, verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction, and realizes privacy protection. At the same time, the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction price. The smart contract is used to complete the verification and game bargaining process, and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction denial. Finally, by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations, it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized environment. The experimental results show that the model improves the credibility and fairness of medical data transactions, maximizes social benefits, encourages more patients and medical institutions to participate in the circulation of medical data, and more fully taps the potential value of medical data.

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Keywords

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