An adaptive privacy-preserving scheme for location tracking of a mobile user

Many popular mobile applications require the continuous monitoring and sharing of a mobile user's location. However, exploiting a user's location leads to disclosing sensitive information about the users daily activity. Several location privacy-preserving schemes have been proposed, but it remains challenging for a user to achieve visibility of the associated threats as well as to control the impact of those threats. This paper presents an adaptive location privacy-preserving system (ALPS) that allows for a user to control the level of privacy disclosure with different quality of location-based service (LBS). We have identified key attack models on location tracking using powerful map-matching algorithms, and then defined a scheme that allows a user to control the privacy of tracking information. We have implemented ALPS on Android OS and evaluated the implementation extensively via trace-based simulation, showing the effectiveness of user-controllable privacy preservation.

[1]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

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

[3]  Marco Gruteser,et al.  Protecting privacy, in continuous location-tracking applications , 2004, IEEE Security & Privacy Magazine.

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

[5]  Marco Gruteser,et al.  Protecting Location Privacy Through Path Confusion , 2005, First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM'05).

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

[7]  Lars Kulik,et al.  Location privacy and location-aware computing , 2006 .

[8]  E. Horvitz,et al.  Map Matching with Travel Time Constraints , 2007 .

[9]  John Krumm,et al.  Inference Attacks on Location Tracks , 2007, Pervasive.

[10]  John Krumm,et al.  A survey of computational location privacy , 2009, Personal and Ubiquitous Computing.

[11]  Pierangela Samarati,et al.  Location privacy in pervasive computing , 2008 .

[12]  Elisa Bertino,et al.  Preventing velocity-based linkage attacks in location-aware applications , 2009, GIS.

[13]  Reza Shokri,et al.  A distortion-based metric for location privacy , 2009, WPES '09.

[14]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

[15]  Panos Kalnis,et al.  Location Diversity: Enhanced Privacy Protection in Location Based Services , 2009, LoCA.

[16]  John Krumm.  Realistic Driving Trips For Location Privacy , 2009, Pervasive.

[17]  Dražen Odobašić.  Open Street Map , 2009 .

[18]  Reza Shokri,et al.  A Unified Framework for Location Privacy , 2010 .

[19]  Suman Nath,et al.  Privacy-Preserving Reconstruction of Multidimensional Data Maps in Vehicular Participatory Sensing , 2010, EWSN.

[20]  Hari Balakrishnan,et al.  Accurate, Low-Energy Trajectory Mapping for Mobile Devices , 2011, NSDI.

[21]  Ramesh Govindan,et al.  Energy-efficient positioning for smartphones using Cell-ID sequence matching , 2011, MobiSys '11.

[22]  Elisa Bertino,et al.  Fine-Grained Cloaking of Sensitive Positions in Location-Sharing Applications , 2011, IEEE Pervasive Computing.

[23]  Jean-Yves Le Boudec,et al.  Quantifying Location Privacy , 2011, 2011 IEEE Symposium on Security and Privacy.

[24]  Kang G. Shin,et al.  Privacy protection for users of location-based services , 2012, IEEE Wireless Communications.