Award Abstract #2027789

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

See grant description on NSF site

Program Manager:

James Joshi

Active Dates:

Awarded Amount:



Jaideep Vaidya

Vivek K Singh

Periklis Papakonstantinou

Stephanie Shiau

Awardee Organization:

Rutgers University Newark
New Jersey


Computer and Information Science and Engineering (CISE)


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.

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