具有中间停靠点的始发-目的地矩阵的差分-私有发布
Differentially-Private Publication of Origin-Destination Matrices with Intermediate Stops
摘要：
传统的起点-目的地（OD）矩阵记录了成对的起点和终点位置之间的行程计数，并已广泛用于交通运输、交通规划等。最近，由于COVID-19大流行病传播建模等用例场景，记录个人路径上的中间点而不仅仅是行程起点和终点变得越来越重要。这可以通过在所需粒度级别的数据空间分区上使用多维频率矩阵来实现。然而，严重的隐私限制时，释放OD矩阵数据，特别是当添加多个中间点，这使得个人轨迹更容易区分的攻击者。为了解决这一威胁，我们提出了一种隐私保护的多维OD矩阵，实现差分隐私（DP），在私人数据发布的事实标准的发布技术。我们提出了一个家庭的方法，因素在重要的数据属性，如数据密度和同质性，以建立OD矩阵，提供可证明的保护保证，同时保持查询的准确性。在真实和合成数据集上的大量实验表明，所提出的方法明显优于现有的最先进的方法。
英文摘要：
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.
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