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A dual model/artificial neural network framework for privacy analysis in traffic monitoring systems

机译:用于交通监控系统中隐私分析的双模型/人工神经网络框架

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摘要

Most large scale traffic information systems rely on a combination of fixed sensors (e.g. loop detectors, cameras) and user generated data, the latter in the form of probe traces sent by smartphones or GPS devices onboard vehicles. While this type of data is relatively inexpensive to gather, it can pose multiple privacy risks, even if the location tracks are anonymous. In particular, an issue could be the possibility for an attacker to infer user location tracks from anonymous location data, which affects users privacy. In this article, we propose a new framework for analyzing a variety of privacy problems arising in transportation systems. The state of traffic is modeled by the Lighthill-Whitham-Richards traffic flow model, which is a first order scalar conservation law with a concave flux function. Given a set of traffic flow data, we show that the constraints resulting from this partial differential equation are mixed integer linear inequalities for some decision variable. These constraints allow us to determine the likelihood of two distinct location tracks being generated by the same vehicle. We then use these model-based reidentification metrics to train an artificial neural network classifier. Numerical implementations are performed on experimental data from the Mobile Century experiment, and show that this framework significantly outperforms naive reidentification techniques.
机译:大多数大型交通信息系统都依赖于固定传感器(例如环路探测器,摄像机)和用户生成的数据的组合,后者以智能手机或车载GPS设备发送的探测轨迹的形式出现。尽管收集此类数据相对便宜,但即使位置跟踪是匿名的,它也可能带来多种隐私风险。特别是,一个问题可能是攻击者有可能从匿名位置数据推断用户位置跟踪,从而影响用户隐私。在本文中,我们提出了一个用于分析运输系统中出现的各种隐私问题的新框架。交通状态由Lighthill-Whitham-Richards交通流模型建模,该模型是具有凹通量函数的一阶标量守恒律。给定一组交通流量数据,我们证明了该偏微分方程产生的约束是某些决策变量的混合整数线性不等式。这些约束条件使我们能够确定同一辆车产生两条不同位置轨迹的可能性。然后,我们使用这些基于模型的重新识别指标来训练人工神经网络分类器。对来自Mobile Century实验的实验数据进行了数值实现,表明该框架明显优于单纯的重新识别技术。

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