TY - JOUR
T1 - On Preventing Location Attacks for Urban Vehicular Networks
AU - Zhou, Meng
AU - Li, Xin
AU - Liao, Lejian
N1 - Publisher Copyright:
© 2016 Meng Zhou et al.
PY - 2016
Y1 - 2016
N2 - The prevalence of global positioning system (GPS) equipped in vehicular networks exposes users' location information to the location-based services. We argue that such data contains rich informative cues on drivers' private behaviors and preferences, which will lead to the location privacy attacks. In this paper, we proposed a sophisticated prediction model to predict driver's next location by using a k-order Markov chain-based third-rank tensor representing the partially observed transfer information of vehicles. Then Bayesian Personalized Ranking (BPR) is used to learn the unobserved transitions within the tensor for transition predication. Experimental results manifest the efficacy of the proposed model in terms of location predication accuracy, compared with several state-of-The-Art predication methods. We also point out that the precision achieved by such advanced predication model is restricted to the order of the Markov chain k. Accordingly, we propose a schema to decrease the risks of such attacks by preventing the conformation of higher order Markov chain. Experimental results obtained based on the real-world vehicular network data demonstrated the effectiveness of our proposed schema.
AB - The prevalence of global positioning system (GPS) equipped in vehicular networks exposes users' location information to the location-based services. We argue that such data contains rich informative cues on drivers' private behaviors and preferences, which will lead to the location privacy attacks. In this paper, we proposed a sophisticated prediction model to predict driver's next location by using a k-order Markov chain-based third-rank tensor representing the partially observed transfer information of vehicles. Then Bayesian Personalized Ranking (BPR) is used to learn the unobserved transitions within the tensor for transition predication. Experimental results manifest the efficacy of the proposed model in terms of location predication accuracy, compared with several state-of-The-Art predication methods. We also point out that the precision achieved by such advanced predication model is restricted to the order of the Markov chain k. Accordingly, we propose a schema to decrease the risks of such attacks by preventing the conformation of higher order Markov chain. Experimental results obtained based on the real-world vehicular network data demonstrated the effectiveness of our proposed schema.
UR - http://www.scopus.com/inward/record.url?scp=85008869260&partnerID=8YFLogxK
U2 - 10.1155/2016/5850670
DO - 10.1155/2016/5850670
M3 - Article
AN - SCOPUS:85008869260
SN - 1574-017X
VL - 2016
JO - Mobile Information Systems
JF - Mobile Information Systems
M1 - 5850670
ER -