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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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Ancile: Enhancing Privacy for Ubiquitous Computing with Use-Based Privacy
Widespread deployment of Intelligent Infrastructure and the In- ternet of Things creates vast troves of passively-generated data. These data enable new ubiquitous computing applications—such as location-based services—while posing new privacy threats. In this work, we identify challenges that arise in applying use-based privacy to passively-generated data, and we develop Ancile, a plat- form that enforces use-based privacy for applications that consume this data. We find that Ancile constitutes a functional, performant platform for deploying privacy-enhancing ubiquitous computing applications.
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- PAR ID:
- 10134021
- Date Published:
- Journal Name:
- Workshop on Privacy in the Electronic Soceity
- Page Range / eLocation ID:
- 111 to 124
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3338498.3358642
