Multiple mix zones de-correlation trajectory privacy model for road network View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Imran Memon, Hamid Turab Mirza, Qasim Ali Arain, Hina Memon

ABSTRACT

Preserving privacy of vehicle movement is an important challenge in road networks; as trajectory data with spatiotemporal information may reveal much individual information. One of the main threats is revealing history location of vehicle trajectories while it stops and again moves toward the destination. Generally, vehicles stop at mostly two places; the first one is traffic light (signal system)/traffic jam and second is at parking locations such as office, shopping mall, home, hospital etc. While existing works only consider social spots. To cope with this issue, we present a new multiple mix zones de-correlation privacy model in which the degree of de-correlation between parking locations and traffic light/traffic jam places. Further, we consider multiple mix zones method to replace parking locations and traffic light/traffic jam places by de-correlation mix zone region. This paper presents an improved privacy traffic monitoring system for road network applications via a proposed security scheme. Specifically, the proposed model analyzes the monitored scene and deployed mix zones parking location and traffic light/traffic jam places. Our method achieved a high privacy level and anonymity solution for trajectory model; moreover, it also balances the service quality and privacy protection. Finally, we performed experiments on real-world data and showed the effectiveness of our method in comparison to existing methods. More... »

PAGES

557-582

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11235-019-00551-1

DOI

http://dx.doi.org/10.1007/s11235-019-00551-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112283254


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