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 JCC  Vol.3 No.5 , May 2015
A User Proprietary Obfuscate System for Positions Sharing in Location-Aware Social Networks
Abstract: A user’s trajectory can be maliciously monitored by adversaries when they share the positions in location-aware social networking applications which require users to update their own locations continuously. An adversary infers user’s locations from the trajectories, and gleans user’s private information through them via location-aware social networking applications and public available geographic data. In this paper, we propose a user proprietary obfuscate system to suit situations for position sharing and location privacy preserving in location-aware social network. Users transform the public available geographic data into personal obfuscate region maps with pre-defined profile to prevent the location leaking in stationary status. Our obfuscation with size restricted regions method tunes user’s transformed locations fitting into natural movement and prevents unreasonable snapshot locations been recorded in the trajectory.
Cite this paper: Cheng, W. and Aritsugi, M. (2015) A User Proprietary Obfuscate System for Positions Sharing in Location-Aware Social Networks. Journal of Computer and Communications, 3, 7-20. doi: 10.4236/jcc.2015.35002.
References

[1]   Pew Research Center (2014) Social Networking Fact Sheet. http://www.pewinternet.org/fact-sheets/social-networking-fact-sheet/

[2]   Wen, M., Li, J., Lei, J.S. and Yang, J.J. (2012) A Light-weight Privacy-Aware Location Query Protocol in Mobile Social Networks. Information and Computational Science, 9, 4429-4437.

[3]   Wernke, M., Durr, F. and Rothermel, K. (2012) PShare: Position Sharing for Location Privacy Based on Multi-Secret Sharing. IEEE International Conference on Pervasive Computing and Communications, Lugano, 19-23 March 2012, 153-161. http://dx.doi.org/10.1109/PerCom.2012.6199862

[4]   Damiani, M.L., Bertino, E. and Silvestri, C. (2010) The PROBE Frame Work for the Personalized Cloaking of Private Locations. Transactions on Data Privacy, 3, 123-148.

[5]   Cho, E., Myers, S.A. and Leskovec, J. (2011) Friendship and Mobility: User Movement in Location-Based Social Networks. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, 21-24 August 2011, 1082-1090. http://dx.doi.org/10.1145/2020408.2020579

[6]   Krumm, J. (2007) Inference Attacks on Location Tracks. Proceedings of the 5th International Conference on Pervasive Computing, Toronto, 13-16 May 2007, 127-143. http://dx.doi.org/10.1007/978-3-540-72037-9_8

[7]   Jain, A.K. and Shanbhag, D. (2012) Addressing Security and Privacy Risks in Mobile Applications. IT Professional, 14, 28-33. http://dx.doi.org/10.1109/MITP.2012.72

[8]   Ball, J. (2014) Angry Birds and “Leaky” Phone Apps Targeted by NSA and GCHQ for User Data. http://www.theguardian.com/world/2014/jan/27/nsa-gchq-smartphone-app-angry-birds-personal-data

[9]   Krishnamurthy, B. and Wills, C.E. (2010) Privacy Leakage in Mobile Online Social Networks. Proceedings of the 3rd Workshop on Online Social Networks, Boston, 22-25 June 2010, 4.

[10]   Wernke, M., Skvortsov, P., Durr, F. and Rothermel, K. (2014) A Classification of Location Privacy Attacks and Approaches. Personal and Ubiquitous Computing, 18, 163-175. http://dx.doi.org/10.1007/s00779-012-0633-z

[11]   Mokbel, M.F., Chow, C.Y. and Aref, W.G. (2006) The New Casper: Query Proc-essing for Location Services without Compromising Privacy. Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, 12-15 September 2006, 763-774.

[12]   Xue, M., Kalnix, P. and Pung, H.K. (2009) Location Diversity: Enhanced Privacy Protection in Location Based Services. Proceedings of the 4th International Symposium on Location and Context Awareness, LoCA’09, 70-87. http://dx.doi.org/10.1007/978-3-642-01721-6_5

[13]   Bamba, B., Liu, L., Pesti, P. and Wang, T. (2008) Supporting Anonymous Location Queries in Mobile Environments with Privacy Grid. Proceedings of the 17th International Conference on World Wide Web, Beijing, 21-25 April 2008, 237-246. http://dx.doi.org/10.1145/1367497.1367531

[14]   Ghinita, G., Damiani, M.L. and Silvestri, C. (2009) Preventing Velocity-Based Linkage Attacks in Location-Aware Applications. Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, 4-6 November 2009, 246-255. http://dx.doi.org/10.1145/1653771.1653807

[15]   Wei, W., Xu, F. and Li, Q. (2012) MobiShare: Flexible Privacy-Preserving Location Sharing in Mobile Online Social Networks. 2012 Proceedings IEEE INFOCOM, Orlando, 25-30 March 2012, 2616-2620. http://dx.doi.org/10.1145/1367497.1367531

[16]   Lin, D., Bertino, E., Cheng, R. and Prabhakar, S. (2009) Location Privacy in Mov-ing-Object Environments. Transactions on Data Privacy, 2, 21-46.

[17]   Cheng, W.C. and Aritsugi, M. (2014) A User Sensitive Privacy-Preserving Location Sharing System in Mobile Social Networks. Procedia Computer Science, 35, 1692-1701. http://dx.doi.org/10.1016/j.procs.2014.08.262

[18]   Carey, N. (2005) Establishing Pedestrian Walking Speeds. Project Report, Portland State University, ITE Student Chapter.

[19]   OGC Technical Committee (1999) Open GIS Simple Features Specification for SQL. Revision 1.1. Open GIS Consortium.

[20]   http://www.openstreetmap.org

 
 
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