Secure transfer of robust healthcare data using blockchain-based privacy

The healthcare sector is continuously evolving, and it is anticipated that the next healthcare model would be information-focused. Cloud computing technology can help the sector handle heterogeneous sources of data and the effective convergence of data. Due to security and privacy considerations, it...

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Bibliographic Details
Published in:	Cluster computing 2024-04, Vol.27 (2), p.1275-1291
Main Authors:	Kumar, Maddila Suresh, Nagalakshmi, Vadlamani
Format:	Article
Language:	eng
Subjects:	Access control Blockchain Cloud computing Computer Communication Networks Computer Science Computer security Consortia Cryptography Data base management systems Data integrity Data transmission Denial of service attacks Electronic health records Health care Health care industry Health facilities Information management Maximum likelihood estimation Medical records Neural networks Operating Systems Particle swarm optimization Patients Personal health Privacy Processor Architectures Radio frequency identification Ransomware Robustness Threats
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Summary:	The healthcare sector is continuously evolving, and it is anticipated that the next healthcare model would be information-focused. Cloud computing technology can help the sector handle heterogeneous sources of data and the effective convergence of data. Due to security and privacy considerations, it is challenging to transfer a medical record from multiple hospital databases. Consequently, the article proposed a blockchain-based patient information management system by using the Blockchain-based Privacy-Preserving and Robust Healthcare data (BPPRH). This transfers the medical data to the patient through the cloud more securely. The cloud is modelled with the hypervisor and VMs. During the data transmission or storage in the cloud, there are several attacks like ransomware, malicious insiders, Man-in-the-middle attack, malware injection attacks, and Denial-of-Service attacks (DoS) occur in the network. Modified Fuzzy Particle Swarm Optimization (MFPSO) is presented to detect these threats. After detecting the attacks, Edge-Cloud-based Collaborative Systems (ECCS) prevent the data from attackers with the use of Regularized Maximum Likelihood Estimation, Inverse Network, and Shadow Model Reconstruction models. The results showed that the system provides better security with high accuracy of 99.32% respectively. The proposed Blockchain-based architecture is designed to contribute to the healthcare management systems’ robustness and to avoid recorded security limitations in commonly used systems for smart healthcare respectively.
ISSN:	1386-7857 1573-7543