A Framework for Generating Network-Based Moving Objects
Geoinformatica
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Modeling and Querying Moving Objects
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Protecting Location Privacy Through Path Confusion
SECURECOMM '05 Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks
A peer-to-peer spatial cloaking algorithm for anonymous location-based service
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Efficient and robust pseudonymous authentication in VANET
Proceedings of the fourth ACM international workshop on Vehicular ad hoc networks
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
Virtual trip lines for distributed privacy-preserving traffic monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
PAM: An Efficient and Privacy-Aware Monitoring Framework for Continuously Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Wireless Location Privacy Protection in Vehicular Ad-Hoc Networks
Mobile Networks and Applications
On the effectiveness of changing pseudonyms to provide location privacy in VANETS
ESAS'07 Proceedings of the 4th European conference on Security and privacy in ad-hoc and sensor networks
Discovering private trajectories using background information
Data & Knowledge Engineering
Anonymization of moving objects databases by clustering and perturbation
Information Systems
P2-CTM: privacy preserving collaborative traffic monitoring
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
Privacy issues in vehicular ad hoc networks
PET'05 Proceedings of the 5th international conference on Privacy Enhancing Technologies
SECURING VEHICULAR COMMUNICATIONS
IEEE Wireless Communications
AMOEBA: Robust Location Privacy Scheme for VANET
IEEE Journal on Selected Areas in Communications
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Collaborative Traffic Monitoring (CTM) systems exploit the location information continuously collected from vehicles. Users collaborate by providing their location information to have a global picture of the current traffic in real-time. However, location information is very sensitive information that made privacy a major obstacle for the widespread usage of CTM systems. Some of these systems depend on periodic location updates, where a vehicle updates location periodically [1]; other systems trigger update at particular regions [2], or with random time periods [3]. For privacy issues, these systems rely on a trusted third party for enforcing a predetermined privacy level. They may also generate low quality data because of the low precision in both time and space [4]. In this paper, we present a privacy aware collaborative traffic monitoring system, PA-CTM, where moving objects send their location updates to a traffic server, the latter then processes current data and provides its users with current traffic status. Users authenticate themselves to traffic server using pseudonyms that are changed according to user's privacy preferences. PA-CTM deploys two mechanisms for enhancing privacy, the first mechanism is the use of pseudonyms (to authenticate to the traffic server) to hide real identities, and changing these pseudonyms to hide trajectory information from the traffic server. Users can control their privacy by frequently changing their pseudonyms and hence become anonymous to traffic server. The second privacy enhancement technique in PA-CTM is the use of a novel autonomous location update mechanism, ALUM. In ALUM, location update is performed according to moving objects' behavior (change in speed or direction) without the need to a trusted third party. Unlike state-of-the art techniques, ALUM does not require a trusted third-party for triggering vehicles to update their locations. We utilized the existence of location prediction errors to calculate the region where a particular vehicle is expected to be in and hence to calculate anonymity level at that region. We compared ALUM against periodic and random silent period update mechanisms and it showed better privacy results in terms of k-anonymity metric.