COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing
COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing
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DOI：
10.1109/globecom42002.2020.9348141
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Rui Chen;Liang Li;Jeffrey Jiarui Chen;Ronghui Hou;Yanmin Gong;Yuanxiong Guo;M. Pan
中科院分区：
文献类型：
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作者：
Rui Chen;Liang Li;Jeffrey Jiarui Chen;Ronghui Hou;Yanmin Gong;Yuanxiong Guo;M. Pan
新冠病毒（COVID - 19）大流行引发了一场前所未有的全球公共卫生危机，世界上大多数国家的医疗资源都快耗尽了。一份精细的新冠病毒易感性地图对于追踪有新冠类似症状的人数至关重要，这样就可以确定潜在的疫情爆发社区，并主动、动态地分配宝贵的医疗资源。基于移动众包的症状报告是构建这样一幅地图的一种有前景且便捷的选择，然而它可能会损害众包参与者的位置隐私。在这项工作中，我们提出了一种新的方法，在不向半诚实的众包聚合器披露参与者位置隐私的情况下，基于众包报告建立新冠病毒易感性地图。简而言之，基于差分隐私的地理不可区分性，移动参与者能够在本地扰动他们的地理数据。利用经过处理的地理信息，我们采用具有空间平滑的最佳线性无偏预测估计器来获得感兴趣区域的可靠易感性估计值并构建地图。鉴于冠状病毒传播迅速的特性，我们将易感性估计值与易感 - 暴露 - 感染 - 康复（SEIR）模型相结合，构建了一幅未来趋势地图。基于真实世界数据的大量模拟验证了所提方法的有效性。
The pandemic of the coronavirus (COVID-19) has caused an unprecedented global public health crisis, and most countries in the world are running out of the healthcare resources. A fine-grained COVID-19 vulnerability map will be essential to track the number of people with covid-like symptoms, so that the the potential outbreak communities can be identified and the valuable healthcare resources can proactively and dynamically be allocated. Mobile crowdsourcing based symptom reporting is a promising and convenient option to construct such a map, while it may compromise the location privacy of crowdsourcing participants. In this work, we propose a novel approach to establish the COVID-19 vulnerability map based on the crowdsourced reporting without disclosing the participants' location privacy to a semi-honest crowdsourcing aggregator. Briefly, based on the differentially private geo-indistinguishability, the mobile participants are able to locally perturb their geographic data. With the masked geographic information, we employ the best linear unbiased prediction estimator with spatial smoothing to obtain the reliable vulnerability estimates in the areas of interest and construct the map. Given the fast spreading nature of coronavirus, we integrate the vulnerability estimates with a susceptible-exposed-infected-removed (SEIR) model to build up a future trend map. Extensive simulations based on real-world data verify the effectiveness of the proposed method.