Blockchain Framework for Secure COVID-19 Pandemic Data Handling and Protection.


Journal

Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357

Informations de publication

Date de publication:
2022
Historique:
received: 03 06 2022
revised: 14 07 2022
accepted: 27 07 2022
entrez: 26 9 2022
pubmed: 27 9 2022
medline: 28 9 2022
Statut: epublish

Résumé

COVID-19 pandemic caused global epidemic infections, which is one of the most severe infections in human medical history. In the absence of proper medications and vaccines, handling the pandemic has been challenging for governments and major health facilities. Additionally, tracing COVID-19 cases and handling data generated from the pandemic are also extremely challenging. Data privacy access and collection are also a challenge when handling COVID-19 data. Blockchain technology provides various features such as decentralization, anonymity, cryptographic security, smart contracts, and a distributed framework that allows users and entities to handle COVID-19 data better. Since the outbreak has made the moral crisis in the clinical and administrative centers worse than any other that has resulted in the decline in the supply of the exact information, however, it is vital to provide fast and accurate insight into the situation. As a result of all these concerns, this study emphasizes the need for COVID-19 data processing to acquire aspects such as data security, data integrity, real-time data handling, and data management to provide patients with all benefits from which they had been denied owing to misinformation. Hence, the management of COVID-19 data through the use of the blockchain framework is crucial. Therefore, this paper illustrates how blockchain technology can be implemented in the COVID-19 data handling process. The paper also proposes a framework with three main layers: data collection layer; data access and privacy layer; and data storage layer.

Identifiants

pubmed: 36156957
doi: 10.1155/2022/7025485
pmc: PMC9492366
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

7025485

Informations de copyright

Copyright © 2022 Arshad Ahmad Dar et al.

Déclaration de conflit d'intérêts

The authors declare that they have no conflicts of interest.

Références

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Auteurs

Arshad Ahmad Dar (AA)

Department of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia.

Malik Zaib Alam (MZ)

Department of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia.

Adeel Ahmad (A)

Department of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia.

Faheem Ahmad Reegu (FA)

Department of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia.

Saima Ahmed Rahin (SA)

United International University, Dhaka, Bangladesh.

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Classifications MeSH