Differentially-Private Publication of Origin-Destination Matrices with Intermediate Stops-猫眼课题宝

DOI：

10.48786/edbt.2022.04

发表时间：

2022-02

期刊：

影响因子：

通讯作者：

中科院分区：

其他

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作者：

Sina Shaham;G. Ghinita;C. Shahabi

传统的起源末期（OD）矩阵记录了两对起点和终点之间的旅行计数，并且由于用例场景（例如Covid-19 ，越来越重要的是，还沿个人的路径记录中间点，而不仅仅是行程的开始和终点。这可以通过在所需粒度水平的数据空间分配上使用多维频率矩阵来实现。但是，在释放OD矩阵数据时，尤其是添加多个中间点时，就会发生严重的隐私限制，这使得单个轨迹对攻击者更有区别。为了应对这一威胁，我们提出了一种技术，以保护具有差异性隐私（DP）的多维OD矩阵的隐私发布，这是私人数据发布中的事实上标准。我们提出了一种考虑重要数据属性（例如数据密度和均匀性）的方法家族，以构建OD矩阵，以提供可证明的保护保证的同时保持查询准确性。对真实和合成数据集进行的广泛实验表明，所提出的方法显然优于现有的最新方法。

Conventional origin-destination (OD) matrices record the count of trips between pairs of start and end locations, and have been extensively used in transportation, traffic planning, etc. More recently, due to use case scenarios such as COVID-19 pandemic spread modeling, it is increasingly important to also record intermediate points along an individual's path, rather than only the trip start and end points. This can be achieved by using a multi-dimensional frequency matrix over a data space partitioning at the desired level of granularity. However, serious privacy constraints occur when releasing OD matrix data, and especially when adding multiple intermediate points, which makes individual trajectories more distinguishable to an attacker. To address this threat, we propose a technique for privacy-preserving publication of multi-dimensional OD matrices that achieves differential privacy (DP), the de-facto standard in private data release. We propose a family of approaches that factor in important data properties such as data density and homogeneity in order to build OD matrices that provide provable protection guarantees while preserving query accuracy. Extensive experiments on real and synthetic datasets show that the proposed approaches clearly outperform existing state-of-the-art.

Differentially-Private Publication of Origin-Destination Matrices with Intermediate Stops

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