Recent technological advances have opened many new possibilities for health appli- cations. Next generation of networks allows real-time monitoring, collaboration, and diagnosis. Machine Learning and Deep Learning enable modeling and understanding complex and enormous datasets. Yet all the innovations also pose new challenges to application designers and maintainers. To deliver high standard e-health services while following regulations, Quality of Service requirements need to be fulfilled, high accuracy needs to be archived, let along all the security defenses to protect sensitive data from leaking. In this thesis, we present a collection of works towards a progressive framework for building secure, responsive, and intelligent e-health applications, focusing on three major components, Analyze, Acquire, and Authenticate. The framework is progres- sive, as it can be applied to various architectures, growing with the project and adapting to its needs. For newer decentralized applications that perform data anal- ysis locally on users’ devices, powerful models outperforming existing solutions can be built using Deep Learning, while Federated Learning provides further privacy guarantee against data leakage, as shown in the case of sleep stage prediction task using smart watch data. For traditional centralized applications performing com- plex computations on the cloud or on-premise clusters, to provide Quality of Service guarantees for the data acquisition process in a sensor network, a delay estimation model based on queueing theory is proposed and verified using simulation. We also explore the novel idea of using molecular communication for authentication, named Molecular Key, enabling the incorporation of environmental information into security policy. We envision this framework can provide stepping stones for future e-health applications.
|Date made available
|KAUST Research Repository