RAPID: Decentralization and Privacy for Secure Vaccination Coordination

该项目的目标是开发和部署一个保护隐私、以用户为中心的数字解决方案，以加强疫苗接种协调，并为研究人员创建一个保护隐私的数据聚合平台。该项目将实现一个分散的端到端协议，涵盖整个疫苗使用过程，从分阶段接种到长期监测不良反应。该团队将从信任、隐私和包容性的角度考虑健康公平。目前的疫苗接种协调系统集中在系统的一个部分，或者要求每个疫苗接种者都有一部智能手机，这加剧了公平问题。该项目通过一系列面向用户的解决方案来解决这些问题：纸质疫苗接种卡上的QR码，可以离线操作，以及移动的手机应用程序，无需实时上网。标准化的数据共享系统在一个平台上整合了全人群和个性化的信息，以提高干预措施的速度和有效性，在这种情况下是疫苗接种，以便对其进行监测和分析。这使得能够鸟瞰网络-物理-社会生态系统，而无需创建监视状态。该项目将推动大流行应对和大流行准备的数据驱动预测分析的界限。该项目的目标是提供一个以用户为中心的解决方案，帮助研究人员和规划人员应对当前和未来的流行病。在这个项目中，用户的行程和相关的去识别数据采集分为四个部分：（i）使用数字签名的优惠券进行分阶段疫苗接种的数字增强型登记系统，（ii）基于隐私保护的QR码的疫苗接种卡，以及与疫苗接种点对接而不向中央服务器泄露任何个人身份信息的智能手机应用程序，（iii）通过数字签名的离线凭证，以防篡改和安全的方式提供疫苗接种证明，（iv）不良反应监测和警报系统，使用户能够以加密认证的方式上传其症状。该项目涉及建立数据汇总和数据传播解决方案，对人口规模和个人规模的分析具有不同程度的粒度。为了保护早期贡献者的隐私，该项目将使用基于安全多方计算的新一代技术。为了传播，该项目将使用麻省理工学院研究人员发明的分裂学习和分裂推理方法，这些方法可能能够更好地解决隐私-效用权衡问题。疫苗接种数据在所有三个层面都至关重要：（i）疫苗的物流和监测;（ii）疫苗接种工作流程;（iii）疫苗接种前后的用户体验。该项目将为今后大流行病和类似性质的紧急情况的有效数据收集监测系统提供工具，而不侵犯个人自由。这些系统和方法是由来自各个机构的流行病学家、工程师、数据科学家、数字隐私布道者、教授和研究人员组成的联盟建立的。这种多样化的合作对于最大限度地减少对现有疫苗接种系统的干扰并确保疫苗接种在全国范围内顺利推广至关重要。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.