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

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.