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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.
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Everything You Always Wanted to Know About Secure and Private Database Systems (but were Afraid to Ask)
Individuals and organizations are accumulating data at an unprecedented rate owing to the advent of inexpensive cloud computing. Data owners are increasingly turning to secure and privacy-preserving collaborative analytics to maximize the value of their records. In this paper, we will survey the state-of-the- art of this growing area. We will describe how researchers are bringing security and privacy-enhancing technologies, such as differential privacy, secure multiparty computation, and zero-knowledge proofs, into the query lifecycle. We also touch upon some of the challenges and opportunities associated with deploying these technologies in the field.
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- PAR ID:
- 10545939
- Editor(s):
- Xiao, Xiaokui
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Data Engineering Bulletin
- Volume:
- 48
- Issue:
- 1
- ISSN:
- 1053-1238
- Page Range / eLocation ID:
- 3-18
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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