Medical Data Privacy Protection Based On Blockchain Asymmetric Encryption Algorithm And Generative Adversarial Network

被引:0
作者
Gao, Yuanyuan [1 ,2 ]
Kim, Jin-whan [1 ]
机构
[1] Youngsan Univ, Dept Comp & Informat Engn, Yangsan Si 50510, Gyeongsangnam D, South Korea
[2] Liaodong Univ, Teaching Qual Assurance & Evaluat Ctr, Dandong 118001, Peoples R China
来源
JOURNAL OF APPLIED SCIENCE AND ENGINEERING | 2025年 / 28卷 / 09期
关键词
medical data privacy protection; blockchain asymmetric encryption algorithm; generative adversarial;
D O I
10.6180/jase.202509_28(9).0009
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页码:1731 / 1738
页数:8
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  • [21] Generative adversarial network in medical imaging: A review
    Yi, Xin
    Walia, Ekta
    Babyn, Paul
    [J]. MEDICAL IMAGE ANALYSIS, 2019, 58
  • [22] An Anomaly Detection Model Based on Deep Auto-Encoder and Capsule Graph Convolution via Sparrow Search Algorithm in 6G Internet of Everything
    Yin, Shoulin
    Li, Hang
    Laghari, Asif Ali
    Gadekallu, Thippa Reddy
    Sampedro, Gabriel Avelino
    Almadhor, Ahmad
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (18): : 29402 - 29411
  • [23] Attribute-based multiparty searchable encryption model for privacy protection of text data
    Yin, Shoulin
    Li, Hang
    Teng, Lin
    Laghari, Asif Ali
    Estrela, Vania Vieira
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (15) : 45881 - 45902
  • [24] LBVP: A Lightweight Batch Verification Protocol for Fog-Based Vehicular Networks Using Self-Certified Public Key Cryptography
    Zhang, Xiaoyu
    Zhong, Hong
    Cui, Jie
    Bolodurina, Irina
    Liu, Lu
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (05) : 5519 - 5533
  • [25] An improved convolution Merkle tree-based blockchain electronic medical record secure storage scheme
    Zhu, Hegui
    Guo, Yujia
    Zhang, Libo
    [J]. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2021, 61
  • [26] Decentralizing Privacy: Using Blockchain to Protect Personal Data
    Zyskind, Guy
    Nathan, Oz
    Pentland, Alex 'Sandy'
    [J]. 2015 IEEE SECURITY AND PRIVACY WORKSHOPS (SPW), 2015, : 180 - 184