RAPID: Collaborative: PPSRC: Privacy-Preserving Self-Reporting for COVID-19

随着国家的重新开放以及经济和其他活动的重新开始，确保警惕地监测COVID-19的传播并相应地灵活调整政策变得重要。技术可以在这一过程中发挥重要作用。该项目旨在了解基于移动应用程序的接触者追踪和症状监测技术如何帮助抗击COVID-19并部署有效的技术。预计通过该项目学到的知识可以成为抗击SARS-CoV-2以及未来新出现的病原体的宝贵工具。这个项目有两个重点。推力1研究基于移动的电话的联系人追踪能在多大程度上提供帮助。第一阶段有三个任务。首先，总结现有提案的设计选择，系统地探索设计空间，并确定潜在的隐私和安全攻击以及可能的防御措施。其次，利用现有的接触者追踪相关数据，了解COVID-19的流行病学特征。第三，使用模拟模型评估技术辅助病例发现和接触者追踪的有效性。Thrust 2旨在通过三项任务开发保护隐私的COVID-19症状监测技术。首先，设计并实现了基于局部差分隐私的隐私保护技术。其次，设计并实现一个用于症状自我报告的App。第三，将从应用程序收集的数据与其他公共来源的数据进行整合，并使用动态状态空间模型对整合后的数据进行建模，为高风险地区提供早期预警。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。

With the reopening of the country and the restarting of economic as well as other activities, it becomes important to ensure vigilantly monitoring the spreading of COVID-19 and nimbly adjusting the policies accordingly. Technologies can play an important role in this process. This project aims at understanding how mobile-app based contact tracing and symptom monitoring technologies can help in fighting COVID-19 and deploying effective technologies. It is expected that knowledge learned through the project can be a valuable tool to fight SARS-CoV-2 as well as future emerging pathogens. This project has two thrusts. Thrust 1 studies to what extent contact tracing based on mobile phones can help. Thrust 1 has three tasks. First, summarize the design choices of existing proposals, systematically explore the design space, and identify potential privacy and security attacks and possible defenses. Second, use existing data related to contact tracing to understand the epidemiological characteristics of COVID-19. Third, assess the effectiveness of technology-aided case finding and contact-tracing using simulation models. Thrust 2 aims at developing Privacy-Preserving COVID-19 Symptom Monitoring Technologies, through three tasks. First, design and implement privacy protection technologies based on local differential privacy. Second, design and implement an App for self-reporting of symptoms. Third, integrate data collected from the app with data from other public sources, and model the integrated data using a dynamic state-space model to provide early warning for high risk districts.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.