| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 16, 2017 |
| Latest Amendment Date: | August 16, 2017 |
| Award Number: | 1722791 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2017 |
| End Date: | August 31, 2022 (Estimated) |
| Total Intended Award Amount: | $413,000.00 |
| Total Awarded Amount to Date: | $413,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1523 UNION RD RM 207 GAINESVILLE FL US 32611-1941 (352)392-3516 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1 University of Florida Gainesville FL US 32611-2002 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Smart and Connected Health |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Although current healthcare systems actively collect medical data from patients in hospitals, numerous personal subjective data is commonly neglected in the analysis of environmental public health due to high-sensitivity of health-related data. As a result, there is a lack of real-time monitoring data, such as symptom reports from high-risk groups and severe environmental pollution, causing notoriously long latency for effective prevention of the spread of epidemic diseases. This project is to address the fundamental challenges on collecting and analyzing multi-scale data from multi-sources for environmental public health in a privacy-preserving manner. The developed technologies empower each individual in a community to proactively contribute real-time data of themselves and surroundings for the betterment of public health without compromising his/her privacy. In addition, this project also serves as a training ground for educating future decision-makers and workforce on privacy-preserving healthcare technologies.
This multidisciplinary research advances the state-of-the-art public health by combining multi-scale data collection and analysis. Specifically, the project redesigns current healthcare monitoring systems for both severe infectious diseases and long-term environment-related diseases and their exacerbation (e.g., air pollutant-induced pulmonary diseases, such as chronic obstructive pulmonary disease and lung cancer). By considering the high sensitivity and distributed manner of the data from patients and users, this project addresses the privacy preservation in two-fold: 1) completely redesign efficient collaborative classification schemes by applying novel metrics without leaking individual's privacy; and 2) introduce new architectures to perform crowdsourcing data analysis by using light-weighted and verifiable encryption schemes. This project also grounds the theoretical outcomes to actual crowdsensing systems and social networks for validation. Finally, a new methodology on public health prediction model is developed with practical systematic implementation in healthcare systems.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Although current healthcare systems actively collect medical data from patients in hospitals and medical facilities, numerous personal subjective data is commonly neglected in the analysis of environmental public health such as in air polluted areas and COVID-19 infected regions, due to high sensitivity of health-related data. As such, there is lack of real-time health monitoring data, such as symptom reports from high-risk groups such as COVID-19 infected population and severe environmental pollution (e.g., due to industrial hazardous waste), causing notoriously long latency for effective prevention of the spread of epidemic diseases. In this project, the PI has addressed the fundamental challenges on collecting and analyzing multi-scale data from multi-sources for environmental public health in a privacy-preserving manner. The developed technologies could enable each individual in a community to proactively contribute real-time data for him/her and his/her surroundings for the betterment of public health without worrying about his/her privacy leakage. In addition, this project has also served as a training ground for educating future health decision-makers to make technically sound decisions when dealing with epidemic/pandemic (e.g., COVID-19) and preparing workforce equipped with privacy-preserving public health technologies.
More specifically, this multidisciplinary project has advanced the state-of-the-art of public health by combining multi-scale data collection and analysis with privacy preservation. It has revisited the current public health monitoring systems with privacy preservation and developed new privacy-preserving monitoring technologies. By considering the high sensitivity and distributed manner of the collected data from patients and users, this project has addressed the privacy preservation in two-fold: 1) completely redesign efficient collaborative classification schemes by applying novel metrics (e.g., similarity) without leaking individual's privacy; and 2) introduce new efficient architectures for both communications and computing to perform crowdsourcing data analysis. It has grounded the theoretical outcomes to actual crowdsensing systems and social networks for validation. Finally, new methodologies on public health prediction models based on powerful distributed machine learning technologies have been developed.
Throughout this project, the PI mentored graduate students with critical thinking skills and brainstorming meetings, which train students to do effective research and become independent researchers. During this project, together with other NSF project support, the PI has graduated 7 PhD students (all of them had become assistant professors). Finally, many high-quality papers have been published, or presented in some of the premier conferences.
Last Modified: 07/21/2022
Modified by: Yuguang Fang
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