A Predictive Differentially-Private Mechanism for Mobility Traces

With the increasing popularity of GPS-enabled handheld devices, location based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their millions of users. Trying to address these issues, the notion of geo-indistinguishability was recently introduced, adapting the well-known concept of Differential Privacy to the area of location-based systems. A Laplace-based obfuscation mechanism satisfying this privacy notion works well in the case of a sporadic use; Under repeated use, however, independently applying noise leads to a quick loss of privacy due to the correlation between the location in the trace.

[1]  Ashwin Machanavajjhala,et al.  Privacy: Theory meets Practice on the Map , 2008, 2008 IEEE 24th International Conference on Data Engineering.

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

[3]  Sébastien Gambs,et al.  Show me how you move and I will tell you who you are , 2010, SPRINGL '10.

[4]  Catuscia Palamidessi,et al.  Broadening the Scope of Differential Privacy Using Metrics , 2013, Privacy Enhancing Technologies.

[5]  Tetsuji Satoh,et al.  Protection of Location Privacy using Dummies for Location-based Services , 2005, 21st International Conference on Data Engineering Workshops (ICDEW'05).

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

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

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

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

[10]  Benjamin C. Pierce,et al.  Distance makes the types grow stronger: a calculus for differential privacy , 2010, ICFP '10.

[11]  Guy N. Rothblum,et al.  A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.

[12]  Xing Xie,et al.  GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory , 2010, IEEE Data Eng. Bull..

[13]  Xing Xie,et al.  T-drive: driving directions based on taxi trajectories , 2010, GIS '10.

[14]  Liviu Iftode,et al.  Privately querying location-based services with SybilQuery , 2009, UbiComp.

[15]  Shen-Shyang Ho,et al.  Differential privacy for location pattern mining , 2011, SPRINGL '11.

[16]  Yu Zhang,et al.  Preserving User Location Privacy in Mobile Data Management Infrastructures , 2006, Privacy Enhancing Technologies.

[17]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[18]  Catuscia Palamidessi,et al.  Geo-indistinguishability: differential privacy for location-based systems , 2012, CCS.

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

[20]  Claude Castelluccia,et al.  Differentially private sequential data publication via variable-length n-grams , 2012, CCS.

[21]  Tim Roughgarden,et al.  Interactive privacy via the median mechanism , 2009, STOC '10.

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

[23]  Manolis Terrovitis,et al.  Privacy preservation in the dissemination of location data , 2011, SKDD.

[24]  Rinku Dewri,et al.  Local Differential Perturbations: Location Privacy under Approximate Knowledge Attackers , 2013, IEEE Transactions on Mobile Computing.

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

[26]  Carmela Troncoso,et al.  Protecting location privacy: optimal strategy against localization attacks , 2012, CCS.

[27]  Moni Naor,et al.  Differential privacy under continual observation , 2010, STOC '10.

[28]  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).

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