Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability
Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability
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DOI：
10.1109/jiot.2022.3158895
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发表时间：
2022-09-15
影响因子：
10.6
通讯作者：
Pan, Miao
中科院分区：
文献类型：
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作者：
Chen, Rui;Li, Liang;Pan, Miao
预防COVID-19疾病在社区传播需要积极有效的医疗资源分配，例如疫苗接种。精细的COVID-19漏洞地图对于检测高风险社区和指导有效的疫苗政策至关重要。基于移动众包的自我报告方法是一个很有前途的解决方案。然而，基于移动众包的精确地图构建要求参与者报告他们的实际位置，这引起了严重的隐私问题。为了解决这个问题，我们提出了一种新的方法，可以在不损害参与者位置隐私的情况下，基于移动的众包COVID-19自我报告，有效地构建可靠的社区级COVID-19漏洞地图。我们设计了一个地理扰动方案，参与者可以在本地混淆他们的位置与地理不可否认性的保证，以保护他们的位置隐私对任何对手的先验知识。为了最小化位置扰动造成的数据效用损失，我们首先设计了一个无偏的脆弱性估计器，并制定了位置扰动概率生成到凸优化。其目标是在地理不可测性约束下，最小化直接易损性估计器的估计误差。鉴于扰动的位置，我们整合的扰动概率与空间平滑方法，以获得可靠的社区一级的脆弱性估计，是强大的小样本量的位置扰动引起的问题。考虑到冠状病毒的快速传播特性，我们将脆弱性估计整合到带有疫苗接种的修正的易感染-感染-清除（SIR）模型中，以构建未来趋势图。它有助于在供应有限时为疫苗分配提供指导。基于真实数据的大量仿真结果表明，该方案在估计精度和可靠性方面优于满足地理不变性的同类设计。
Preventing COVID-19 disease from spreading in communities will require proactive and effective healthcare resource allocations, such as vaccinations. A fine-grained COVID-19 vulnerability map will be essential to detect the high-risk communities and guild the effective vaccine policy. A mobile-crowdsourcing-based self-reporting approach is a promising solution. However, an accurate mobile-crowdsourcing-based map construction requests participants to report their actual locations, raising serious privacy concerns. To address this issue, we propose a novel approach to effectively construct a reliable community-level COVID-19 vulnerability map based on mobile crowdsourced COVID-19 self-reports without compromising participants' location privacy. We design a geo-perturbation scheme where participants can locally obfuscate their locations with the geo-indistinguishability guarantee to protect their location privacy against any adversaries' prior knowledge. To minimize the data utility loss caused by location perturbation, we first design an unbiased vulnerability estimator and formulate the location perturbation probability generation into a convex optimization. Its objective is to minimize the estimation error of the direct vulnerability estimator under the constraints of geo-indistinguishability. Given the perturbed locations, we integrate the perturbation probabilities with the spatial smoothing method to obtain reliable community-level vulnerability estimations that are robust to a small-sampling-size problem incurred by location perturbation. Considering the fast-spreading nature of coronavirus, we integrate the vulnerability estimates into the modified susceptible-infected-removed (SIR) model with vaccination for building a future trend map. It helps to provide a guideline for vaccine allocation when supply is limited. Extensive simulations based on real-world data demonstrate the proposed scheme superiority over the peer designs satisfying geo-indistinguishability in terms of estimation accuracy and reliability.