Medical data privacy protection based on blockchain asymmetric encryption algorithm and generative adversarial network

Yuanyuan  Gao; Jin-whan  Kim

doi:10.6180/jase.202509_28(9).0009

Medical data privacy protection based on blockchain asymmetric encryption algorithm and generative adversarial network

Computer Science and Information Engineering

Yuanyuan Gao^1,2 and Jin-whan Kim¹This email address is being protected from spambots. You need JavaScript enabled to view it.

¹Department of Computer and Information Engineering, Youngsan University Yangsan-si(50510), Gyeongsangnam-do, Republic of Korea

²Teaching Quality Assurance and Evaluation Center, Liaodong University Dandong 118001 China

Received: October 22, 2024
Accepted: November 20, 2024
Publication Date: December 28, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202509_28(9).0009

As sensitive data, medical data is easy to be leaked or maliciously altered to form medical disputes. The high dimensional information of medical data can easily lead to "dimensional disaster". In order to avoid the impact of information attributes on privacy protection and improve the security of personal information, this paper proposes a medical data privacy protection method based on blockchain asymmetric encryption algorithm and generative adversarial network. The improved kernel principal component analysis method is used to reduce the dimension of personal information, reduce the information attribute dimension, and input the personal information after dimensionality reduction into the cyclic consistency generative adversarial network to eliminate the noise data in the information. In the blockchain environment, asymmetric encryption algorithms are used to generate private keys and public keys to encrypt user privacy data. Comprehensive user information, user behavior and user upload public key, evaluate user identity trust, and finally realize user privacy protection through user identity authentication and private data access process control. The experimental results show that the proposed method has high efficiency, good fault tolerance and can effectively protect the security of patients’ personal information.

Keywords: medical data privacy protection, blockchain asymmetric encryption algorithm, generative adversarial network, kernel principal component analysis

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