CT-ISG: Collaborative Research: A Context-Aware Approach to the Design and Evaluation of Privacy Preservation Techniques in Location-Based Services

Sponsored by NSF Cybertrust Program


Sushil Jajodia and Claudio Bettini, CSIS, George Mason University, Fairfax, Virginia
X. Sean Wang, Computer Science Department, The University of Vermont, Burlington, Vermont


Privacy protection challenges arising from location-based services (LBS) are critical to users as well as service providers. This project concentrates on designing and evaluating privacy protection techniques in LBS. The important departure of this project from the existing research is in its emphasis of the role of request contexts. A context refers to the external information/knowledge that the attacker may use, together with the requests themselves, to gain user private information. For example, with the external knowledge of a user’s approximate location at a particular time, the attacker may single out the user of a particular LBS request and thus link the private information in the request to the user. By its nature, context changes from requests to requests and different contexts may call for different privacy protection techniques. The technical objectives of the project are therefore to (1) systematically categorize privacy contexts, (2) analyze existing defense strategies, and (3) design and evaluate new defense strategies.  From an educational perspective, the project will (1) involve graduate students and expose them to leading-edge researches, and (2) incorporate research results into classrooms. In addition, the project will provide a platform for active collaborations among a broader set of researchers of the two institutions. The results from this project will have a positive impact on protecting user privacy and on people's willingness to adopt LBS in enhancing their living and working conditions. Research results will be disseminated through technical reports, publications at conferences and journals, and the websites at http://csis.gmu.edu/NSFLBSprivacy and at  http://www.cs.uvm.edu/~xywang/NSFLBSprivacy.