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.
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A Blockchain Future for Secure Clinical Data Sharing: A Position Paper
In the digital healthcare era, it is utmost important to harness medical information scattered across healthcare institutions to support in-depth data analysis. However, the boundaries of cyberinfrastructure of healthcare providers place obstacles on data sharing. In this position paper, we firstly identify the challenges of medical data sharing and management. Then we introduce the background and give a brief survey on the state-of-the-art. Finally, we conclude the paper by discussing a few possible research directions to cope with the challenges in current medical information sharing.
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- PAR ID:
- 10104758
- Date Published:
- Journal Name:
- Proceedings of the ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization
- Page Range / eLocation ID:
- 23 to 27
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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