论文标题
空间点过程数据的隐私
Privacy for Spatial Point Process Data
论文作者
论文摘要
在这项工作中，我们开发了将空间位置数据私有化的方法，例如单个疾病病例的空间位置。我们提出了两种新型的贝叶斯方法，用于基于log-gaussian Cox过程（LGCP）生成合成位置数据。我们表明，对于点过程数据，可以轻松获得有条件的预测纵坐标（CPO）估计值。我们构建了一种新颖的风险指标，该指标利用CPO估计来评估个人披露风险。我们适应LGCP的倾向均方根误差（PMSE）数据实用性度量。我们证明，与径向合成相比，我们的合成方法与约翰·斯诺（John Snow）博士的霍乱疫情数据相比，提供了改善的风险与公用事业平衡。
In this work we develop methods for privatizing spatial location data, such as spatial locations of individual disease cases. We propose two novel Bayesian methods for generating synthetic location data based on log-Gaussian Cox processes (LGCPs). We show that conditional predictive ordinate (CPO) estimates can easily be obtained for point process data. We construct a novel risk metric that utilizes CPO estimates to evaluate individual disclosure risks. We adapt the propensity mean square error (pMSE) data utility metric for LGCPs. We demonstrate that our synthesis methods offer an improved risk vs. utility balance in comparison to radial synthesis with a case study of Dr. John Snow's cholera outbreak data.
