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

RAPID：COVID-19 的隐私保护群体感知及其社会学和流行病学影响

• 批准号: 2027789
• 负责人: Jaideep Vaidya
• 金额: $ 19.96万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027789&HistoricalAwards=false
• 关键词: RAPID; Privacy; Preserving; Crowdsensing; COVID

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.

成功遏制像新冠肺炎这样的大流行需要有能力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，但测试资源的缺乏和由此产生的测试不足大大阻碍了这一努力。移动人群感知是一种替代技术方法，如果被相当一部分人口使用，在这种情况下可能是有效的。然而，对隐私的担忧以及与大流行相关的耻辱被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向用户和政策制定者提供洞察。该项目还旨在更广泛地了解隐私和极端情况下的决策，并了解人类如何珍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新冠肺炎大流行。更多地向用户传播本地化信息可以从心理角度帮助鼓励社会疏远，从而促进社会中个人的福祉。该项目将从社会认知的角度更好地理解隐私，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新冠肺炎集群，并实时衡量它们的增长。在该项目的努力中，调查人员将研究隐私的数学保证和隐私决策的社会方面，具体到这一背景。为了为用户提供隐私保护，将制定适当的隐私定义，放宽差别隐私，并制定相应的隐私机制。该项目将利用现有文献的见解，使用户能够就共享他们的私人信息做出明智的决定，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健康的重要问题，并通过汇集来自隐私、众感、传播和流行病学的专家来发展新的协同效应。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Identifying Anomalies While Preserving Privacy

• DOI: 10.1109/tkde.2021.3129633
• 发表时间: 2023-12
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: H. Asif;Jaideep Vaidya;Periklis A. Papakonstantinou