Abstract

This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain’s transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain’s consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.

Details

Title
The role of blockchain to secure internet of medical things
Author
Ghadi, Yazeed Yasin 1 ; Mazhar, Tehseen 2 ; Shahzad, Tariq 3 ; Amir khan, Muhammad 4 ; Abd-Alrazaq, Alaa 5 ; Ahmed, Arfan 5 ; Hamam, Habib 6 

 Al Ain University, Department of Computer Science and Software Engineering, Abu Dhabi, UAE (GRID:grid.444473.4) (ISNI:0000 0004 1762 9411) 
 Virtual University of Pakistan, Department of Computer Science, Lahore, Pakistan (GRID:grid.444943.a) (ISNI:0000 0004 0609 0887) 
 COMSATS University Islamabad, Department of Computer Science, Sahiwal, Pakistan (GRID:grid.418920.6) (ISNI:0000 0004 0607 0704) 
 Universiti Teknologi MARA, School of Computing Sciences, College of Computing, Informatics and Mathematics, Shah Alam, Malaysia (GRID:grid.412259.9) (ISNI:0000 0001 2161 1343) 
 AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar (GRID:grid.416973.e) (ISNI:0000 0004 0582 4340) 
 Université de Moncton, Faculty of Engineering, Moncton, Canada (GRID:grid.265686.9) (ISNI:0000 0001 2175 1792); University of Johannesburg, School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, Johannesburg, South Africa (GRID:grid.412988.e) (ISNI:0000 0001 0109 131X); Hodmas University College, Taleh Area, Mogadishu, Somalia (GRID:grid.412988.e); Bridges for Academic Excellence, Tunis, Tunisia (GRID:grid.412988.e) 
Pages
18422
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3090750330
Copyright
© The Author(s) 2024. corrected publication 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.