HARNESSING PRIVACY-PRESERVING FEDERATED LEARNING WITH BLOCKCHAIN FOR SECURE IOMT APPLICATIONS IN SMART HEALTHCARE SYSTEMS

Amal K. Alkhalifa, Meshari H. Alanazi, Khalid Mahmood, Wafa Sulaiman Almukadi, Mohammed Al Qurashi, Asma Hassan Alshehri, Fuhid Alanazi and Abdelmoneim Ali Mohamed
Additional contact information
Amal K. Alkhalifa: Department of Computer Science and Information Technology, Applied College Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Meshari H. Alanazi: ��Department of Computer Science, College of Sciences, Northern Border University, Arar, Saudi Arabia
Khalid Mahmood: ��Department of Information Systems, Applied College at Mahayil King Khalid University, Muhayel Aseer 62529, Saudi Arabia
Wafa Sulaiman Almukadi: �Department of Software Engineering, College of Engineering and Computer Science, University of Jeddah, Saudi Arabia
Mohammed Al Qurashi: �Department of Computer Science, Faculty of Computing and Information, Al-Baha University, Saudi Arabia
Asma Hassan Alshehri: ��Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
Fuhid Alanazi: *Department of Information Systems, Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia
Abdelmoneim Ali Mohamed: ��†Department of Information Systems, College of Computer & Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-17

Abstract: The Internet of Medical Things (IoMT) refers to interconnected medical systems and devices that gather and transfer healthcare information for several medical applications. Smart healthcare leverages IoMT technology to improve patient diagnosis, monitoring, and treatment, providing efficient and personalized healthcare services. Privacy-preserving Federated Learning (PPFL) is a privacy-enhancing method that allows collaborative method training through distributed data sources while ensuring privacy protection and keeping the data decentralized. In the field of smart healthcare, PPFL enables healthcare professionals to train machine learning algorithms jointly on their corresponding datasets without sharing sensitive data, thereby maintaining confidentiality. Within this framework, anomaly detection includes detecting unusual events or patterns in healthcare data like unexpected changes or irregular vital signs in patient behaviors that can represent security breaches or potential health issues in the IoMT system. Smart healthcare systems could enhance patient care while protecting data confidentiality and individual privacy by incorporating PPFL with anomaly detection techniques. Therefore, this study develops a Privacy-preserving Federated Learning with Blockchain-based Smart Healthcare System (PPFL-BCSHS) technique in the IoMT environment. The purpose of the PPFL-BCSHS technique is to secure the IoMT devices via the detection of abnormal activities and FL concepts. Besides, BC technology can be applied for the secure transmission of medical data among the IoMT devices. The PPFL-BCSHS technique employs the FL for training the model for the identification of abnormal patterns. For anomaly detection, the PPFL-BCSHS technique follows three major processes, namely Mountain Gazelle Optimization (MGO)-based feature selection, Bidirectional Gated Recurrent Unit (BiGRU), and Sandcat Swarm Optimization (SCSO)-based hyperparameter tuning. A series of simulations were implemented to examine the performance of the PPFL-BCSHS method. The empirical analysis highlighted that the PPFL-BCSHS method obtains improved security over other approaches under various measures.

Keywords: Internet of Medical Things; Federated Learning; Blockchain; Sandcat Swarm Optimization; Feature Selection (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400201

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