IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain

Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2023-02, Vol.27 (2), p.823-834
Main Authors: Samuel, Omaji, Omojo, Akogwu Blessing, Onuja, Abdulkarim Musa, Sunday, Yunisa, Tiwari, Prayag, Gupta, Deepak, Hafeez, Ghulam, Yahaya, Adamu Sani, Fatoba, Oluwaseun Jumoke, Shamshirband, Shahab
Format: Article
Language:English
Subjects:
Artificial intelligence
Blockchain
Blockchains
Computer Security
Coronaviruses
COVID-19
Cryptography
Data privacy
Delivery of Health Care
epidemiology
Federated learning
Health care
Humans
information security
Information technology
Infrastructure
Medical devices
Medical diagnosis
Medical diagnostic imaging
Medical electronics
Medical equipment
Pandemics
Privacy
Public health
reinforcing addition
Security
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media to disseminate COVID-19 information. Also, the proposed infrastructure can effectively provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2022.3143576