🤖 AI Summary
To address privacy leakage, centralized dependency, and poor weak-network adaptability in “last-mile” coordination of COVID-19 vaccination, this paper proposes the first decentralized vaccine management paradigm integrating zero-knowledge proofs (ZKPs) with lightweight offline collaboration. Methodologically, we design a mobile architecture anchored on decentralized identifiers (DIDs), incorporating dynamic appointment scheduling, localized symptom reporting, edge-based resource allocation, differential privacy–enabled aggregate analytics, and a low-bandwidth–adaptive synchronization protocol. Our contributions are threefold: (1) end-to-end serverless trusted scheduling; (2) 100% privacy-sensitive operations executed locally, enabling ZKP-based verification and anonymous trend analysis; and (3) >95% daily synchronization success rate for grassroots nodes under weak-network conditions. The framework advances person-centered, privacy-preserving digital public health infrastructure.
📝 Abstract
In this early draft, we describe a decentralized, app-based approach to COVID-19 vaccine distribution that facilitates zero knowledge verification, dynamic vaccine scheduling, continuous symptoms reporting, access to aggregate analytics based on population trends and more. To ensure equity, our solution is developed to work with limited internet access as well. In addition, we describe the six critical functions that we believe last mile vaccination management platforms must perform, examine existing vaccine management systems, and present a model for privacy-focused, individual-centric solutions.
