CISE-MSI: RCBP-RF: SaTC: Privacy Preserving Models Leveraging Mobility Data for Public Health

CISE-MSI：RCBP-RF：SaTC：利用移动数据促进公共卫生的隐私保护模型

基本信息

批准号：
2131164
负责人：
Hongmei Chi
金额：
$ 30万
依托单位：
Florida Agricultural and Mechanical University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131164&HistoricalAwards=false
关键词：
CISE MSI RCBP RF SaTC

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The use of health applications on mobile devices is becoming increasingly popular. With that popularity comes a desire to use the mobility data that is generated for various public health purposes, such as contact tracing during COVID-19. It is also used in more complex applications that use machine learning to infer health risks. On one hand, these models promise a transformative impact on targeted public health interventions. On the other hand, results from these models could compromise the privacy of an individual’s health status without directly using health data. Even when the mobility data is de-identified, privacy can be compromised when physical observations of persons’ locations augment the models’ results. People who are considered visible minorities are particularly vulnerable if they come from groups with a disproportionate prevalence of a certain disease. There are ways to adjust privacy techniques that can help mitigate privacy risks, however, they could compromise the accuracy of the models. There is a need for solutions that can yield effective public health models while preserving privacy. Results from this project will be the development of infection spread models that can do just that – give accurate place-based data without compromising privacy for health related applications. The project will use a establish and understanding of effective approaches for co-designing privacy and security techniques with infection spread modeling. These privacy protection approaches would account for potential compromise through physical observations in combination with queries to the models. We will also produce a synthetic population for Northwest Florida designed for efficient updates through data assimilation. Such synthetic-data-driven models have the potential to yield accurate results while preserving privacy. The impact on research and education will be seen in the developing of research capacity at FAMU as well as through interdisciplinary research tasks to be conducted by undergraduate students that are traditionally underrepresented in computing. Results from this project will help expand the pathways into computing fields and other interdisciplinary careersThis 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.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。随着移动设备的普及，人们希望使用移动数据。一方面，这些模型有望对有针对性的公共卫生干预措施产生变革性影响。另一方面，这些模型的结果可能会在不直接使用健康数据的情况下损害个人健康状况的隐私，即使移动数据被取消识别，当对人员位置的物理观察增强了模型的结果时，隐私也会受到损害。如果少数群体来自某种疾病患病率不成比例的群体，那么他们就特别容易受到影响。有一些方法可以调整隐私技术，有助于减轻隐私风险，但它们可能会损害模型的准确性，因此需要解决方案。可以产生有效的公共卫生模型，同时保护隐私。该项目将开发感染传播模型，该模型可以做到这一点——在不损害健康相关应用程序隐私的情况下提供准确的基于地点的数据。该项目将使用建立和理解有效方法来共同设计感染的隐私和安全技术。这些隐私保护方法将通过物理观察与模型查询相结合来解决潜在的妥协问题，我们还将为佛罗里达州西北部生成一个合成群体，旨在通过数据同化进行有效更新。在保护隐私的同时产生准确结果的潜力。研究和教育将体现在 FAMU 研究能力的发展以及由传统上在计算机领域代表性不足的本科生进行的跨学科研究任务中，该项目的结果将有助于拓展进入计算机领域和其他跨学科职业的途径。授予 NSF 的法定使命，并通过评估反映使用基金会的智力优点和更广泛的影响审查标准，被认为值得支持。