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: 2029685
• 负责人: Yuanxiong Guo
• 金额: $ 10万
• 依托单位: University of Texas at San Antonio
• 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=2029685&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.

最近新冠肺炎的大流行已经给美国和世界上许多其他国家造成了公共卫生危机。由于目前还没有治疗新冠肺炎的药物，最具挑战性的问题是如何将有限的医疗资源有效地分配到下一个潜在的暴发社区或地区，以便我们能够阻止甚至阻止病毒的进一步传播。目前依赖人们去看医生或检测站收集感染数据的反应性努力可能不会以有效和及时的方式发挥作用，因为这些地方的覆盖范围有限，成本高，暴露风险增加。该项目通过移动众包开发了细粒度和保护位置隐私的COVID19症状地图(CSM)，症状可能包括发烧、咳嗽或呼吸急促。拟议的CSM在识别潜在暴发地区和促进卫生保健资源的主动和动态分配方面很有希望。同时，位置隐私保护功能将保护移动众包参与者免受偏见或歧视，鼓励他们参与公共利益。该项目涉及移动众包、公共卫生科学、数据分析、隐私和社会科学的协同努力。教育和外联活动旨在增加妇女和少数群体对科学和工程的参与。该项目为位置隐私保护、移动众包、数据分析、社会科学和公共卫生科学开发了一个跨学科框架，并包含一个具有四个相互关联的推进的研究计划。首先，提出了一种新的位置差异化隐私保护机制--差异化私有六边形层次化地理空间索引系统(DPH3)，该机制能够很好地表示地图上的物理社区结构，保证移动众包参与者的位置差异化隐私，并在地图覆盖率和准确性方面为CSM建设提供高移动众包效用。其次，设计了一种基于DPH3和近似算法的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分隐私。第三，将病毒传播模型作为常规克立格法中去趋势的先验知识，并利用稳健估计来减轻DPH3添加的位置隐私保护噪声的影响，从而构建质量保证的CSM。第四，从社会科学角度对社区参与的战略传播方法将被用来激发社区成员寻找和分享有关新冠肺炎症状的信息的积极性，促进CSM所需的众包，以及赋予社区成员权力。该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Geo-Indistinguishablility for Crowdsourced-Based Radio Environment Map Construction

基于众包的无线电环境地图构建的地理不可区分性

• DOI: 10.1109/globecom42002.2020.9348142
• 发表时间: 2020
• 期刊: 2020 IEEE Global Communications Conference
• 作者: Amin, Shahira;Li, Liang;Guo, Yuanxiong;Pan, Miao;Gong, Yanmin
• 通讯作者: Gong, Yanmin

Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization

• DOI: 10.24963/ijcai.2021/202
• 发表时间: 2020-08
• 作者: Rui Hu;Yanmin Gong;Yuanxiong Guo

Concentrated Differentially Private Federated Learning With Performance Analysis

• DOI: 10.1109/ojcs.2021.3099108
• 发表时间: 2021
• 期刊: IEEE Open Journal of the Computer Society
• 影响因子: 5.9
• 作者: Rui Hu;Yuanxiong Guo;Yanmin Gong

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

Private Empirical Risk Minimization with Analytic Gaussian Mechanism for Healthcare System

医疗保健系统的分析高斯机制的私人经验风险最小化

• DOI: 10.1109/tbdata.2020.2997732
• 发表时间: 2020
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Ding, Jiahao;Errapotu, Sai Mounika;Guo, Yuanxiong;Zhang, Haixia;Yuan, Dongfeng;Pan, Miao
• 通讯作者: Pan, Miao