TY - GEN
T1 - A framework for privacy and security analysis of probe-based traffic information systems
AU - Canepa, Edward S.
AU - Claudel, Christian G.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2013
Y1 - 2013
N2 - 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 Mobile Century experiment to validate this framework. © 2013 ACM.
AB - 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 Mobile Century experiment to validate this framework. © 2013 ACM.
UR - http://hdl.handle.net/10754/564644
UR - http://dl.acm.org/citation.cfm?doid=2461446.2461451
UR - http://www.scopus.com/inward/record.url?scp=84877596386&partnerID=8YFLogxK
U2 - 10.1145/2461446.2461451
DO - 10.1145/2461446.2461451
M3 - Conference contribution
SN - 9781450319614
SP - 25
EP - 31
BT - Proceedings of the 2nd ACM international conference on High confidence networked systems - HiCoNS '13
PB - Association for Computing Machinery (ACM)
ER -