Global privacy and transportation mode homogeneity anonymization in location based mobile systems with continuous queries

A major concern for deployment of location-based mobile systems is the ill-usage of mobile client's location data, which may imply sensitive and private personal information. Also, even if the location is exposed willingly by the mobile client the query should not be linked to the mobile client. Still, many location based systems (store finders, transit itinerary systems, and social networks) are created with a different focus and have little concern for end user privacy. We focused on location based mobile systems where the location of the mobile user may be available; however, an adversary should not be able to link a query to a specific mobile user. Two key contributions of this work are the introduction and experimental evaluation of a novel concept called transportation mode homogeneity anonymization that adds another dimension to privacy in mobile location based systems. Also, a novel dynamic layered approach on achieving K-anonymity by separating the local privacy requirement on each snapshot and global privacy requirement across snapshots with different privacy goals is proposed to exploit the local privacy anonymization group as candidates to obtain global anonymization group candidates.

[1]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[2]  Ling Liu,et al.  Supporting anonymous location queries in mobile environments with privacygrid , 2008, WWW.

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

[4]  Wei-Ying Ma,et al.  Understanding mobility based on GPS data , 2008, UbiComp.

[5]  Henry Kautz,et al.  Building Personal Maps from GPS Data , 2006, Annals of the New York Academy of Sciences.

[6]  Panos Kalnis,et al.  Private queries in location based services: anonymizers are not necessary , 2008, SIGMOD Conference.

[7]  Philip S. Yu,et al.  Mobile systems location privacy: “MobiPriv” a robust k anonymous system , 2010, 2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications.

[8]  Jianliang Xu,et al.  Distortion-based anonymity for continuous queries in location-based mobile services , 2009, GIS.

[9]  Hua Lu,et al.  SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services , 2008, 2008 IEEE 24th International Conference on Data Engineering.

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

[11]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.

[12]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[13]  Pierangela Samarati,et al.  Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression , 1998 .

[14]  ASHWIN MACHANAVAJJHALA,et al.  L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[15]  Ying Cai,et al.  Location anonymity in continuous location-based services , 2007, GIS.

[16]  Ling Liu,et al.  Location Privacy in Mobile Systems: A Personalized Anonymization Model , 2005, 25th IEEE International Conference on Distributed Computing Systems (ICDCS'05).

[17]  Tetsuji Satoh,et al.  An anonymous communication technique using dummies for location-based services , 2005, ICPS '05. Proceedings. International Conference on Pervasive Services, 2005..

[18]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[19]  Chi-Yin Chow,et al.  A peer-to-peer spatial cloaking algorithm for anonymous location-based service , 2006, GIS '06.

[20]  Ling Liu,et al.  Privacy-Aware Mobile Services over Road Networks , 2009, Proc. VLDB Endow..

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