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The healthcare industry has experienced a re-markable digital transformation through the adoption of IoT technologies, resulting in a significant increase in the volume and variety of medical data generated. Challenges in processing, analyzing, and sharing healthcare data persist. Traditional cloud computing approaches, while useful for processing healthcare data, have drawbacks, including delays in data transfer, data privacy concerns, and the risk of data unavailability. In this paper, we propose a software-defined 5G and AI-enabled distributed edge-cloud collaboration platform to classify healthcare data at the edge devices, facilitate realtime service delivery, and create AI/ML-based models for identifying patients' potential medical conditions. In our architecture, we have incorporated a federated learning scheme based on homomorphic encryption to provide privacy in data sharing and processing. The proposed framework ensures secure and efficient data communication and processing, ultimately fostering effective collaboration among healthcare institutions. The models will be validated by performing a comparative time analysis, and the interplay between edge and cloud computing will be investigated to support realtime healthcare applications.more » « lessFree, publicly-accessible full text available March 21, 2025
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Chatterjee, Pushpita ; Das, Debashis ; Banerjee, Sourav ; Ghosh, Uttam ; Mpembele, Armando B ; Rogers, Tamara ( , ACM)The Internet of Medical Things (IoMT) is a network of interconnected medical devices, wearables, and sensors integrated into healthcare systems. It enables real-time data collection and transmission using smart medical devices with trackers and sensors. IoMT offers various benefits to healthcare, including remote patient monitoring, improved precision, and personalized medicine, enhanced healthcare efficiency, cost savings, and advancements in telemedicine. However, with the increasing adoption of IoMT, securing sensitive medical data becomes crucial due to potential risks such as data privacy breaches, compromised health information integrity, and cybersecurity threats to patient information. It is necessary to consider existing security mechanisms and protocols and identify vulnerabilities. The main objectives of this paper aim to identify specific threats, analyze the effectiveness of security measures, and provide a solution to protect sensitive medical data. In this paper, we propose an innovative approach to enhance security management for sensitive medical data using blockchain technology and smart contracts within the IoMT ecosystem. The proposed system aims to provide a decentralized and tamper-resistant plat- form that ensures data integrity, confidentiality, and controlled access. By integrating blockchain into the IoMT infrastructure, healthcare organizations can significantly enhance the security and privacy of sensitive medical data.more » « less