Virtual trip lines for distributed privacy-preserving traffic monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
How to protect privacy in floating car data systems
Proceedings of the fifth ACM international workshop on VehiculAr Inter-NETworking
Dirichlet Problems for some Hamilton-Jacobi Equations with Inequality Constraints
SIAM Journal on Control and Optimization
Stealthy deception attacks on water SCADA systems
Proceedings of the 13th ACM international conference on Hybrid systems: computation and control
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Convex Formulations of Data Assimilation Problems for a Class of Hamilton-Jacobi Equations
SIAM Journal on Control and Optimization
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Most large scale traffic information systems rely on fixed sensors (e.g. loop detectors, cameras) and user generated data, this latter in the form of GPS traces sent by smartphones or GPS devices onboard vehicles. While this type of data is relatively inexpensive to gather, it can pose multiple security and privacy risks, even if the location tracks are anonymous. In particular, creating bogus location tracks and sending them to the system is relatively easy. This bogus data could perturb traffic flow estimates, and disrupt the transportation system whenever these estimates are used for actuation. In this article, we propose a new framework for solving a variety of privacy and cybersecurity 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 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. The resulting framework is very flexible, and can in particular be used to detect spoofing attacks in real time, or carry out attacks on location tracks. Numerical implementations are performed on experimental data from the~\emph{Mobile Century} experiment to validate this framework.