RAPID: Privacy-Preserving Crowdsensing of COVID-19 and its Sociological and Epidemiological Implications

成功遏制COVID-19等大流行病需要能够记录感染的存在并跟踪其在社区内的传播。虽然测试是收集此类信息的主要来源，但测试资源的缺乏以及由此产生的测试不足严重阻碍了这一努力。移动的人群感知是一种替代技术方法，如果被很大一部分人口使用，在这种情况下可以有效。然而，隐私问题以及与大流行病有关的耻辱证明是阻碍以这种方式准确收集信息的巨大障碍。该项目的目标是开发一个基础设施和平台，从人口中收集数据，并将其提炼成汇总信息，为用户和政策制定者提供洞察力，同时保护隐私。该项目还旨在更广泛地了解极端情况下的隐私和决策，并了解人类如何重视他们的隐私以及他们在这种情况下所做的选择。该项目将能够收集其他方式无法获得的实时数据，并将能够更有效地应对COVID-19疫情。增加向用户传播本地化信息，从心理角度看有助于鼓励保持社交距离，从而促进社会中个人的福祉。从社会认知角度对隐私的理解将从该项目中获得，这将提高未来开发的数据隐私解决方案的质量。该项目将开发一种人群感知工具，该工具将使用自我报告的症状来有效识别新的COVID-19集群，并实时测量其增长情况。在项目工作中，研究人员将研究隐私的数学保证和隐私决策的社会方面，具体到这种情况。为了给用户提供隐私保护，将制定适当的隐私定义，放宽差别隐私和相应的隐私机制。该项目将利用现有文献中的见解，使用户能够在分享其私人信息方面做出明智的决定，并产生有关极端健康情况下人类隐私行为的新知识。该项目还创建了一个研究基础设施，以支持和研究有关隐私和公共健康的重要问题，并通过汇集隐私，人群感知，通信和流行病学的专家来开发新的协同效应。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 successful containment of pandemics such as COVID-19 requires the ability to record the presence of infections and track its spread within communities. While testing is the primary source to collect such information, the lack of testing resources and the resultant under-testing significantly hampers this effort. Mobile crowdsensing is an alternative technological approach that can be effective in such situations if used by a significant fraction of the population. However, privacy concerns as well as the stigma associated with the pandemic prove to be huge barriers that inhibit the accurate collection of information in this way. The goal of this project is to develop an infrastructure and platform to collect data from the population and distill it into aggregate information to provide insight to both users and policymakers while protecting privacy. The project also aims to gain a broader understanding of privacy and decision making in extreme situations and learn how humans value their privacy and the choices they make in such situations. The project will enable the collection of real-time data, which is not available otherwise, and will enable a more effective response to the COVID-19 pandemic. The increased dissemination of localized information to users can help encourage social distancing from a psychological perspective and thus contribute to the well-being of individuals in society. The improved understanding of privacy from a socio-cognitive perspective to be gained from this project will improve the quality of data privacy solutions that are developed in the future. The project will develop a crowdsensing tool that will use self-reported symptoms to effectively identify new clusters of COVID-19 and measure their growth in real-time. Within the project effort, the investigators will study both mathematical guarantees of privacy and the social aspects of privacy decision making, specific to this context. To provide privacy protection for users an appropriate definition of privacy that relaxes differential privacy and corresponding privacy mechanisms will be developed. The project will utilize insights from extant literature to enable users to make an informed decision regarding sharing their private information and also generate new knowledge regarding human privacy behavior in extreme health scenarios. The project also creates a research infrastructure to support and study important questions regarding privacy and public health, and develops new synergies by bringing together experts from privacy, crowdsensing, communication, and epidemiology.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.