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

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.