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

成功遏制COVID-19等大流行病需要有能力记录感染情况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，但测试资源的缺乏和由此导致的测试不足严重阻碍了这一努力。移动人群感知是一种可替代的技术方法，如果在这种情况下被很大一部分人口使用，它可能是有效的。然而，隐私问题以及与大流行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，以便在保护隐私的同时为用户和政策制定者提供见解。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自己的隐私以及在这种情况下做出的选择。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大流行。从心理角度来看，向用户更多地传播本地化信息有助于鼓励保持社会距离，从而促进社会中个人的福祉。从这个项目中获得的从社会认知角度对隐私的更好理解将提高未来开发的数据隐私解决方案的质量。该项目将开发一种群众感知工具，利用自我报告的症状有效识别新的COVID-19群集，并实时衡量其增长情况。在这个项目中，研究人员将研究隐私的数学保证和隐私决策的社会方面，具体到这个背景下。为了给用户提供隐私保护，将制定适当的隐私定义，放宽差异隐私和相应的隐私机制。该项目将利用现有文献的见解，使用户能够在分享其私人信息方面做出明智的决定，并产生关于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卫生的重要问题，并通过汇集隐私、群体感知、通信和流行病学方面的专家，开发新的协同效应。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 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.