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

• 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

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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助的（公共法117-2）。移动设备上对健康应用的使用越来越流行。有了这个流行的愿望，希望使用用于各种公共卫生目的的流动数据，例如Covid-19期间的接触跟踪。它也用于使用机器学习来推断健康风险的更复杂的应用中。一方面，这些模型有助于对有针对性的公共卫生干预措施产生变革性的影响。另一方面，这些模型的结果可能会损害个人健康状况的隐私，而无需直接使用健康数据。即使将移动性数据取消识别，当人们对人们的位置的物理观察增强模型的结果时，隐私也会受到损害。如果被认为是可见的少数民族的人，如果他们来自某种疾病患病率不成比例的群体，那么他们特别容易受到伤害。有一些方法可以调整隐私技术可以帮助减轻隐私风险，但是，它们可能会损害模型的准确性。有必要在保护隐私的同时产生有效的公共卫生模型的解决方案。该项目的结果将是感染差异模型的开发，这些模型可以做到这一点 - 提供准确的基于位置的数据而不损害与健康相关的应用程序的隐私。该项目将使用对有效方法的建立和理解，用于通过感染建模来共同设计隐私和安全技术。这些隐私保护方法将通过与模型的查询结合进行物理观察来解释潜在的妥协。我们还将为西北佛罗里达州生产一个合成人群，旨在通过数据同化为有效的更新。这样的合成数据驱动的模型有可能在保留隐私的同时产生准确的结果。在FAMU的研究能力以及通过跨学科的研究任务的发展中，将看到对研究和教育的影响，这些任务是由传统上在计算中代表不足的本科生进行的。该项目的结果将有助于将途径扩展到计算领域和其他跨学科职业，该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Forecasting COVID-19 Hotspots in Florida Public Schools: A Machine Learning Approach

预测佛罗里达州公立学校的 COVID-19 热点：机器学习方法

• DOI: 10.1109/bigdata59044.2023.10386102
• 发表时间: 2023
• 期刊: IEEE
• 作者: Peng, Mingming;Ali, Askal Ayalew;Chi, Hongmei
• 通讯作者: Chi, Hongmei

Ontology-guided Attribute Learning to Accelerate Certification for Developing New Printing Processes

• DOI: 10.1080/24725854.2023.2263786
• 发表时间: 2023-09
• 期刊: IISE Transactions
• 影响因子: 2.6
• 作者: Tsegai O. Yhdego;Hongya Wang;Zhibin Yu;Hongmei Chi

Investigating Gender and Racial Bias in ELECTRA

调查 ELECTRA 中的性别和种族偏见

• DOI: 10.1109/csci58124.2022.00027
• 发表时间: 2022
• 期刊: IEEE
• 作者: Taeb, Maryam;Torres, Yonathan;Chi, Hongmei;Bernadin, Shonda
• 通讯作者: Bernadin, Shonda