Compromising location privacies for vehicles cloud computing

In this paper, we propose an enhanced vehicular crowdsourcing localisation and tracking (EVCLT) scheme for mounting a trajectory tracking attack in vehicular cloud computing environment. In our scheme, crowdsourcing technique is applied to sample the location information of certain users. Then matrix completion technique is used to generate our predictions of the users' trajectories. To alleviate the error disturbance of the recovered location data, Kalman filter technique is implemented and the trajectories of certain users are recovered with accuracy. At last, extensive simulations are conducted to show the performance of our scheme. Simulation results reveal that the proposed approach is able to accurately track the trajectories of certain users.

[1]  Peng Hao,et al.  Probabilistic model for estimating vehicle trajectories using sparse mobile sensor data , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[2]  Christian Laugier,et al.  Growing Hidden Markov Models: An Incremental Tool for Learning and Predicting Human and Vehicle Motion , 2009, Int. J. Robotics Res..

[3]  Xu Guan,et al.  Robustness Evaluation of Decentralized Self-Information Dissemination Control Algorithms for VANET Tracking Applications , 2009, 2009 IEEE 70th Vehicular Technology Conference Fall.

[4]  Rong Du,et al.  VANET based traffic estimation: A matrix completion approach , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[5]  Shaojie Tang,et al.  Data gathering in wireless sensor networks through intelligent compressive sensing , 2012, 2012 Proceedings IEEE INFOCOM.

[6]  Hariharan Krishnan,et al.  Adaptive intervehicle communication control for cooperative safety systems , 2010, IEEE Network.

[7]  Victor C. M. Leung,et al.  Connectivity-aware minimum-delay geographic routing with vehicle tracking in VANETs , 2011, Ad Hoc Networks.

[8]  Sherali Zeadally,et al.  Vehicular ad hoc networks (VANETS): status, results, and challenges , 2010, Telecommunication Systems.

[9]  Raja Sengupta,et al.  Tracking the position of neighboring vehicles using wireless communications , 2010 .

[10]  K. Suriyapaibonwattana,et al.  An Effective Safety Alert Broadcast Algorithm for VANET , 2008, 2008 International Symposium on Communications and Information Technologies.

[11]  Lin Yao,et al.  An anonymous authentication scheme in data-link layer for VANETs , 2016, Int. J. Ad Hoc Ubiquitous Comput..

[12]  Fan Bai,et al.  Mobile Vehicle-to-Vehicle Narrow-Band Channel Measurement and Characterization of the 5.9 GHz Dedicated Short Range Communication (DSRC) Frequency Band , 2007, IEEE Journal on Selected Areas in Communications.

[13]  李向阳,et al.  Communicating Is Crowdsourcing： Wi-Fi Indoor Localization with CSI-Based Speed Estimation , 2014 .

[14]  Michel Barbeau,et al.  Non-cooperating vehicle tracking in VANETs using the conditional logit model , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[15]  Bo Xu,et al.  VCLT: An Accurate Trajectory Tracking Attack Based on Crowdsourcing in VANETs , 2015, ICA3PP.

[16]  Feng Xia,et al.  A Clustering-Based Location Privacy Protection Scheme for Pervasive Computing , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[17]  Chi Lin,et al.  Protecting location privacy and query privacy: a combined clustering approach , 2015, Concurr. Comput. Pract. Exp..

[18]  Emmanuel J. Candès,et al.  Exact Matrix Completion via Convex Optimization , 2009, Found. Comput. Math..

[19]  Y. Zhang,et al.  Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization , 2014, Optim. Methods Softw..

[20]  Lin Yao,et al.  Location Anonymity Based on Fake Queries in Continuous Location-Based Services , 2012, 2012 Seventh International Conference on Availability, Reliability and Security.

[21]  Luca Delgrossi,et al.  IEEE 802.11p: Towards an International Standard for Wireless Access in Vehicular Environments , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[22]  Yong Feng,et al.  Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone , 2013, Int. J. Distributed Sens. Networks.

[23]  Qian Zhang,et al.  Compressive sensing based monitoring with vehicular networks , 2013, 2013 Proceedings IEEE INFOCOM.

[24]  R. Poovendran,et al.  CARAVAN: Providing Location Privacy for VANET , 2005 .

[25]  Ce-Kuen Shieh,et al.  Improving positioning accuracy for VANET in real city environments , 2014, The Journal of Supercomputing.

[26]  Minglu Li,et al.  A novel vehicular location prediction based on mobility patterns for routing in urban VANET , 2012, EURASIP J. Wirel. Commun. Netw..

[27]  Katharina Morik,et al.  Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management , 2014, EDBT.

[28]  S. N. Sivanandam,et al.  Location Identification and Vehicle Tracking using VANET ( VETRAC) , 2007 .

[29]  Masao Kuwahara,et al.  Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data , 2011 .

[30]  Ghassan Samara,et al.  Security issues and challenges of Vehicular Ad Hoc Networks (VANET) , 2010, 4th International Conference on New Trends in Information Science and Service Science.