Secure Mix-Zones for Privacy Protection of Road Network Location Based Services Users

Privacy has been found to be the major impediment and hence the area to be worked out for the provision of Location Based Services in the wide sense. With the emergence of smart, easily portable, communicating devices, information acquisition is achieving new domains. The work presented here is an extension of the ongoing work towards achieving privacy for the present day emerging communication techniques. This work emphasizes one of the most effective real-time privacy enhancement techniques called Mix-Zones. In this paper, we have presented a model of a secure road network with Mix-Zones getting activated on the basis of spatial as well as temporal factors. The temporal factors are ascertained by the amount of traffic and its flow. The paper also discusses the importance of the number of Mix-Zones a user traverses and their mixing effectiveness. We have also shown here using our simulations which are required for the real-time treatment of the problem that the proposed transient Mix-Zones are part of a viable and robust solution towards the road network privacy protection of the communicating moving objects of the present scenario.

[1]  Jamalul-lail Ab Manan,et al.  Privacy preservation in Location-Based Services (LBS) through Trusted Computing technology , 2009, 2009 IEEE 9th Malaysia International Conference on Communications (MICC).

[2]  Mohamed F. Mokbel,et al.  Identifying Unsafe Routes for Network-Based Trajectory Privacy , 2009, SDM.

[3]  Yufei Tao,et al.  Continuous Nearest Neighbor Search , 2002, VLDB.

[4]  Karim Emara.  Location privacy in vehicular networks , 2013, 2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[5]  Chi-Yin Chow,et al.  Trajectory privacy in location-based services and data publication , 2011, SKDD.

[6]  Panos Kalnis,et al.  Providing K-Anonymity in location based services , 2010, SKDD.

[7]  Aris Gkoulalas-Divanis,et al.  A k-Anonymity Model for Spatio-Temporal Data , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[8]  Panagiotis Papadimitratos,et al.  VeSPA: vehicular security and privacy-preserving architecture , 2013, HotWiSec '13.

[9]  Agusti Solanas,et al.  Privacy Protection in Location-Based Services Through a Public-Key Privacy Homomorphism , 2007, EuroPKI.

[10]  Yuzhe Tang,et al.  Location Privacy with Road Network Mix-Zones , 2012, 2012 8th International Conference on Mobile Ad-hoc and Sensor Networks (MSN).

[11]  Chi-Yin Chow,et al.  Enabling Private Continuous Queries for Revealed User Locations , 2007, SSTD.

[12]  Nikos Pelekis,et al.  HERMES: aggregative LBS via a trajectory DB engine , 2008, SIGMOD Conference.

[13]  Frank Stajano,et al.  Location Privacy in Pervasive Computing , 2003, IEEE Pervasive Comput..

[14]  Sushil Jajodia,et al.  Anonymity in Location-Based Services: Towards a General Framework , 2007, 2007 International Conference on Mobile Data Management.

[15]  Bart Preneel,et al.  Towards Measuring Anonymity , 2002, Privacy Enhancing Technologies.

[16]  Walid G. Aref,et al.  Casper*: Query processing for location services without compromising privacy , 2006, TODS.

[17]  Mark Ryan,et al.  Analysing Unlinkability and Anonymity Using the Applied Pi Calculus , 2010, 2010 23rd IEEE Computer Security Foundations Symposium.

[18]  Vijayalakshmi Atluri,et al.  Ensuring Privacy and Security for LBS through Trajectory Partitioning , 2010, 2010 Eleventh International Conference on Mobile Data Management.

[19]  Reza Shokri,et al.  On the Optimal Placement of Mix Zones , 2009, Privacy Enhancing Technologies.

[20]  Jimeng Sun,et al.  The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries , 2003, VLDB.

[21]  Marco Gruteser,et al.  USENIX Association , 1992 .

[22]  Hassan Takabi,et al.  A collaborative k-anonymity approach for location privacy in location-based services , 2009, 2009 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing.

[23]  Zhendong Ma,et al.  Privacy in inter-vehicular networks: Why simple pseudonym change is not enough , 2010, 2010 Seventh International Conference on Wireless On-demand Network Systems and Services (WONS).

[24]  Ernesto Damiani,et al.  Location Privacy Protection Through Obfuscation-Based Techniques , 2007, DBSec.

[25]  Martin E. Hellman,et al.  Probability of error, equivocation, and the Chernoff bound , 1970, IEEE Trans. Inf. Theory.

[26]  Miao Pan,et al.  Traffic-aware multiple mix zone placement for protecting location privacy , 2012, 2012 Proceedings IEEE INFOCOM.

[27]  Lars Kulik,et al.  A Formal Model of Obfuscation and Negotiation for Location Privacy , 2005, Pervasive.

[28]  Brejesh Lall,et al.  Privacy Protection Through k.anonymity in Location.based Services , 2012 .

[29]  Raja Lavanya,et al.  Communication Efficient Distributed Decentralized Key Management Framework for Message Authentication in Vanet , 2012 .

[30]  Aris Gkoulalas-Divanis,et al.  A Free Terrain Model for Trajectory K-Anonymity , 2008, DEXA.