@inproceedings{a5f06a703a1d47d99271d6bec5576864,
title = "Toward a Secure Healthcare Ecosystem: A Convergence of Edge Analytics, Blockchain, and Federated Learning",
abstract = "Modern healthcare organizations face various cy-bersecurity threats due to the digitization of their systems. These threats include data breaches, ransomware attacks, and unauthorized access to patients' sensitive information, which constitute real challenges for the healthcare ecosystem. To tackle these challenges, advanced security measures must be employed. They enable real-Time analysis of crucial data and precise threat identification and provide robust protection for valuable data assets. This paper proposes an integrative approach and a system architecture for cybersecurity in healthcare, allowing real-Time threat detection and data protection. The approach integrates the three innovative technologies of edge analytics, blockchain technology, and federated learning. Ensuring the cybersecurity of Electronic Health Records (EHRs) is an illustrative use case of the proposed architecture. Furthermore, the paper proposes a set of tools that can be used for the implementation of the architecture.",
keywords = "Cybersecurity, Data sharing, Model training, Privacy, Security, Smart contract",
author = "Elarbi Badidi and Hanane Lamaazi and Harrouss, {Omar El}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 20th International Conference on the Design of Reliable Communication Networks, DRCN 2024 ; Conference date: 06-05-2024 Through 09-05-2024",
year = "2024",
doi = "10.1109/DRCN60692.2024.10539174",
language = "English",
series = "20th International Conference on the Design of Reliable Communication Networks, DRCN 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "20th International Conference on the Design of Reliable Communication Networks, DRCN 2024",
}