AI-Driven Privacy-Preserving Contact Tracing and Pandemic Response Systems
Abstract
The COVID-19 pandemic exposed weaknesses in traditional contact-tracing and data-sharing systems, where centralized architectures often compromise privacy and response speed. This paper proposes a federated learning-based contact-tracing framework integrated with zero-trust network architecture to support secure, privacy-preserving, and scalable public-health monitoring.
Type
Publication
International Journal of Engineering and Technical Research (IJETR)
This research introduces a federated learning and zero-trust security model for real-time pandemic response across distributed health networks.
