RAPID: SafePaths: A privacy-first contact tracing solution for early interventions of COVID-19 spread during the first wave and to minimize the second wave of the epidemic

该项目的目标是开发和部署隐私优先的数字解决方案，用于公共卫生协调，包括接触者追踪，以遏制COVID-19等大流行病的传播。关键是为市民和公共卫生专家提供地点和背景。目前的做法是在隐私和有效性之间权衡，依赖于一般的公共广播，这给提取的信息带来了不确定性，或者诉诸侵犯隐私的技术，使个人权利面临被污名化和监视的风险。该项目将打破这种二分法，开发一种基于技术的解决方案，通过接触者追踪来协调有关感染和可能传播的信息，同时保护病毒携带者和未接触者的隐私权。除了通过追踪接触者帮助遏制COVID-19大流行之外，该项目还将为计算、医疗保健、危机应对等领域做出实证贡献。隐私保护是这个项目的关键方面，接触追踪是通过使用加密的GPS轨迹和旋转的蓝牙标识符来实现的。在这种方法中，感染者的编辑信息只被共享，而健康人的信息不会离开设备。具体而言，该项目将推进以下方面的知识：1.)如何通过易于使用的应用程序在智能手机等无处不在的平台上实现加密技术，以有效地使用私有化数据而不会泄露任何敏感信息；2)社会危机的个人技术解决方案如何有效地影响行为，从而影响这种危机的结果；3)如何利用健康、人口统计、旅行历史、空间背景和现实世界参与等个人信息实施资源高效的分布式人工智能技术“分裂学习”，以执行私人风险评估和接触者追踪，以降低误报率。这个解决方案是由一个由流行病学家、工程师、数据科学家、数字隐私传道者、教授和来自知名机构的研究人员组成的联盟制定的。这对于减少对社会经济活动的干扰和在应对未来紧急情况时将恐慌控制在合理可控的水平至关重要。该奖项反映了美国国家科学基金会的法定使命，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-first digital solution for public health coordination including contact-tracing to curb pandemics like COVID-19 spread. The key is to provide location and context for citizens and public health experts. Current approaches operate on a trade-off between privacy and effectiveness, relying on general public broadcasting that introduces uncertainty in the information extracted or resorting to privacy-violating technologies that risk individual rights against stigmatization and surveillance. This project will break past this dichotomy by developing a technology-based solution for coordinating information on infection and possible transmission through contact-tracing while protecting the privacy rights of viral carriers and unexposed citizens.Beyond assisting the containment of COVID-19 pandemic by contact tracing, this project will make empirical contributions to the fields of computing, healthcare, crisis response, and more. With privacy preservation being the key aspect of this project, contact tracing is achieved by using encrypted GPS trails and rotating Bluetooth identifiers. In this approach, redacted information of an infected individual is only shared while no information leaves the device of a healthy person. Specifically, this project will advance knowledge regarding: 1.) how cryptographic techniques can be implemented on ubiquitous platforms like smart phones through easy to use apps to efficiently use privatized data without leakage of any sensitive information; 2) how personal-technology solutions to societal crises can effectively influence behavior and consequently affect the outcome of such crises; and 3) how “split-learning”, a resource efficient distributed AI technique can be implemented with personal information on health, demographic, travel history, spatial context, and real-world engagement to perform private risk-assessment post contact-tracing to reduce false alarm rates. The solution is being built by a consortium of epidemiologists, engineers, data scientists, digital privacy evangelists, professors and researchers from reputable institutions. This is crucial to reduce disruption in socio-economic activity and keep panic under rationally controllable levels in response to future emergencies.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.