Towards End-to-End Privacy-Preserving AI for Real-Time ECG Monitoring

Healthcare artificial intelligence is shifting from centralized archives toward distributed pipelines that combine wearables, edge processing, cloud analytics, and multi-institutional learning. In this regime, electrocardiogram (ECG) streams cross multiple trust boundaries, so privacy cannot be reduced to a single transmission control. Existing privacy-enhancing technologies typically secure isolated stages and leave gaps between acquisition, analysis, and institutional collaboration. This doctoral research develops a unified lifecycle framework for end-to-end privacy-preserving ECG AI. Rather

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