RAPID: Decentralization and Privacy for Secure Vaccination Coordination

The objective of this project is to develop and deploy a privacy-protecting, user-centric, digital solution to enhance vaccination coordination and to create a privacy-preserving, data-aggregation platform for researchers. The project will enable a decentralized, end-to-end protocol that spans the entire vaccine user journey, from enrollment in phased vaccination to long term monitoring of adverse effects.

The team will consider health equity from the perspective of trust, privacy, and inclusivity. Current systems for vaccination coordination focus on a single part of the system or require a smartphone for every vaccine recipient which aggravates the equity concerns. This project addresses such concerns through an array of user-facing solutions: QR codes on paper vaccination cards which can operate offline as well as mobile phone apps without live internet access.

The standardized data sharing system consolidates both population-wide and individualized information in a single platform to increase the speed and effectiveness of the intervention, vaccination in this case, so that it can be monitored and analyzed. This enables a bird’s eye view of the cyber-physical-social ecosystem without creating a surveillance state.

The project will push the boundaries of data-driven predictive analytics for pandemic response and pandemic preparedness.

The project’s goal is to provide a user-centric solution that can aid researchers and planners of the current and future pandemics. In this project, the user’s journey and the relevant de-identified data collection is divided into four parts: (i) Digitally enhanced enrollment system for phased vaccination using digitally signed coupons, (ii) A privacy-preserving QR code based vaccination card, and a smartphone app to interface with vaccination sites without revealing any personally identifiable information to centralized servers, (iii) Proof of vaccination in a tamper-evident and secure manner available with digitally signed offline credentials, (iv) Monitoring and alert systems for adverse reactions that enable users to upload their symptoms in a cryptographically authenticated manner.

The project involves building data aggregation and data dissemination solutions with varying levels of granularity for population-scale and individual scale analysis. For aggregation, to preserve the privacy of early contributors, the project will use a new generation of techniques based on secure multi-party computation. For dissemination, the project will use Split Learning and Split Inference methods invented by the investigator at MIT that may be able to better address privacy-utility trade-offs.

Vaccination data is critical at all three levels: (i) logistics and monitoring of vaccines (ii) vaccination workflows and (iii) user experience before and after vaccination. This project will generate tools for an efficient data gathering monitoring system for future pandemics and emergencies of a similar nature without invasion of personal freedoms.

The systems and methods are being built by a consortium of epidemiologists, engineers, data scientists, digital privacy evangelists, professors, and researchers from various institutions. Such a diverse collaboration is essential to minimize disruption to the existing vaccination system and ensure a smooth vaccination roll-out across the nation.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.