RAPID: Collaborative: Location Privacy Preserving COVID-19 Symptom Map Construction via Mobile Crowdsourcing for Proactive Constrained Resource Allocation

RAPID：协作：通过移动众包构建位置隐私保护 COVID-19 症状图，以实现主动的受限资源分配

• 批准号: 2029569
• 负责人: Miao Pan
• 金额: $ 10万
• 依托单位: University of Houston
• 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 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029569&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Location; Privacy; Preserving

The recent pandemic of COVID-19 has caused a public health crisis to US and many other countries in the world. Since there is currently no medication to treat COVID-19, the most challenging question is how to effectively allocate constrained healthcare resources to the next potential outburst communities or areas, so that we can halt or even prevent the virus' further spreading. The current reactive efforts of relying on people to visit their doctors or testing stations for collecting infection data may not work in an efficient and timely manner due to the limited coverage, high cost, and increased exposure risk for people to be congested at those places. This project develops a fine-grained and location privacy-preserving COVID19 symptom map (CSM) via mobile crowdsourcing, where the symptoms may include fever, cough, or shortness of breath. The proposed CSM is promising in its ability to identify the potential outbreak areas and facilitate the proactive and dynamic allocation of healthcare resources. Meanwhile, the location privacy preserving feature will protect the mobile crowdsourcing participants from bias or discrimination, encouraging them to participate for the public good. The project involves synergized efforts from mobile crowdsourcing, public health science, data analytics, privacy, and social science. Education and outreach activities are designed to increase the participation of women and minority in science and engineering.This project develops an interdisciplinary framework for location privacy preservation, mobile crowdsourcing, data analytics, social science, and public health science, and contains a research plan with four interconnected thrusts. First, a novel location differential privacy preservation mechanism named differentially private hexagonal hierarchical geospatial indexing system (DPH3) will be developed, which can well represent physical community structure in the map, guarantee mobile crowdsourcing participants’ location differential privacy, and provide high mobile crowdsourcing utility for CSM construction in terms of map coverage and accuracy. Second, a coverage-aware crowdsourcing participant recruitment scheme based on DPH3 and approximation algorithm will be designed to guarantee the crowdsensing coverage while preserving participants' location differential privacy. Third, a quality-assured CSM will be constructed by using virus propagation model as the prior knowledge for detrending in ordinary kriging and robust estimation for mitigating the impact of location privacy preserving noises added by DPH3. Fourth, a strategic communication approach to community engagement from the perspective of social science will be used to motivate community members’ activeness in information seeking and sharing about COVID-19 symptoms, facilitate crowdsourcing needed for the CSM, as well as empower the community members.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.

近期COVID-19大流行已对美国及全球多个其他国家造成公共卫生危机。由于目前没有治疗COVID-19的药物，最具挑战性的问题是如何有效地将有限的医疗资源分配到下一个潜在的爆发社区或地区，以便我们能够阻止甚至防止病毒的进一步传播。目前依靠人们去看医生或检测站收集感染数据的反应性努力可能无法以有效和及时的方式工作，因为覆盖范围有限，成本高，并且人们在这些地方拥挤的暴露风险增加。该项目通过移动的众包开发了一个细粒度和位置隐私保护的COVID 19症状地图（CSM），其中症状可能包括发烧，咳嗽或呼吸急促。拟议的CSM在识别潜在爆发地区和促进积极和动态分配医疗资源方面具有很大的潜力。同时，位置隐私保护功能将保护移动的众包参与者免受偏见或歧视，鼓励他们参与公益。该项目涉及移动的众包、公共卫生科学、数据分析、隐私和社会科学的协同努力。教育和推广活动旨在提高妇女和少数民族在科学和工程领域的参与度。该项目为位置隐私保护、移动的众包、数据分析、社会科学和公共卫生科学制定了一个跨学科框架，并包含一个包含四个相互关联的重点的研究计划。首先，提出了一种新的位置差异隐私保护机制--差异隐私六边形分层地理空间索引系统（DPH 3），该机制能够很好地表示地图中的物理社区结构，保证移动的众包参与者的位置差异隐私，并在地图覆盖率和精确度方面为CSM构建提供较高的移动的众包效用。其次，基于DPH 3和近似算法设计了一个覆盖感知的众包参与者招募方案，在保证众包覆盖的同时保护参与者的位置差异隐私。第三，一个有质量保证的CSM将被构造通过使用病毒传播模型作为先验知识的普通克里格和鲁棒估计，以减轻由DPH 3添加的位置隐私保护噪声的影响去趋势。第四，从社会科学的角度出发，采用社区参与的战略传播方法，激励社区成员积极寻求和分享有关COVID-19症状的信息，促进CSM所需的众包，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，更广泛的影响审查标准。

项目成果

COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing

• DOI: 10.1109/globecom42002.2020.9348141
• 发表时间: 2020-12
• 期刊: GLOBECOM 2020 - 2020 IEEE Global Communications Conference
• 作者: Rui Chen;Liang Li;Jeffrey Jiarui Chen;Ronghui Hou;Yanmin Gong;Yuanxiong Guo;M. Pan

Analyzing Social Distancing and Seasonality of COVID-19 with Mean Field Evolutionary Dynamics

用平均场进化动力学分析 COVID-19 的社交距离和季节性

• DOI: 10.1109/gcwkshps50303.2020.9367567
• 发表时间: 2020
• 期刊: 2020 IEEE Globecom Workshops
• 作者: Gao, Hao;Li, Wuchen;Pan, Miao;Han, Zhu;Poor, H. Vincent
• 通讯作者: Poor, H. Vincent

Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability

• DOI: 10.1109/jiot.2022.3158895
• 发表时间: 2022-09-15
• 期刊: IEEE INTERNET OF THINGS JOURNAL
• 影响因子: 10.6
• 作者: Chen, Rui;Li, Liang;Pan, Miao
• 通讯作者: Pan, Miao

IoT Device Friendly and Communication-Efficient Federated Learning via Joint Model Pruning and Quantization

• DOI: 10.1109/jiot.2022.3145865
• 发表时间: 2022-08-01
• 期刊: IEEE INTERNET OF THINGS JOURNAL
• 影响因子: 10.6
• 作者: Prakash, Pavana;Ding, Jiahao;Pan, Miao
• 通讯作者: Pan, Miao

Modeling COVID-19 with mean field evolutionary dynamics: Social distancing and seasonality

使用平均场进化动力学对 COVID-19 进行建模：社交距离和季节性

• DOI: 10.23919/jcn.2021.000032
• 发表时间: 2021
• 期刊: Journal of Communications and Networks
• 影响因子: 3.6
• 作者: Gao, Hao;Li, Wuchen;Pan, Miao;Han, Zhu;Poor, H. Vincent
• 通讯作者: Poor, H. Vincent