Location Privacy in Pervasive Computing
IEEE Pervasive Computing
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Enhancing Security and Privacy in Traffic-Monitoring Systems
IEEE Pervasive Computing
Algorithms
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Learning and inferring transportation routines
Artificial Intelligence
Preserving location privacy in wireless lans
Proceedings of the 5th international conference on Mobile systems, applications and services
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
IEEE Transactions on Knowledge and Data Engineering
Preserving privacy in gps traces via uncertainty-aware path cloaking
Proceedings of the 14th ACM conference on Computer and communications security
ICMB '08 Proceedings of the 2008 7th International Conference on Mobile Business
Identification via location-profiling in GSM networks
Proceedings of the 7th ACM workshop on Privacy in the electronic society
On the Anonymity of Home/Work Location Pairs
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Hiding stars with fireworks: location privacy through camouflage
Proceedings of the 15th annual international conference on Mobile computing and networking
On the Optimal Placement of Mix Zones
PETS '09 Proceedings of the 9th International Symposium on Privacy Enhancing Technologies
Efficient algorithms to solve Bayesian Stackelberg games for security applications
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A distortion-based metric for location privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
Faking contextual data for fun, profit, and privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
A Game Theoretical Model for Adversarial Learning
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Moving forward: location privacy and location awareness
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
Stackelberg games for adversarial prediction problems
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Quantifying location privacy: the case of sporadic location exposure
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
Journal of Artificial Intelligence Research
Evaluating the privacy risk of location-based services
FC'11 Proceedings of the 15th international conference on Financial Cryptography and Data Security
Geo-indistinguishability: differential privacy for location-based systems
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Optimal sporadic location privacy preserving systems in presence of bandwidth constraints
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
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The mainstream approach to protecting the location-privacy of mobile users in location-based services (LBSs) is to alter the users' actual locations in order to reduce the location information exposed to the service provider. The location obfuscation algorithm behind an effective location-privacy preserving mechanism (LPPM) must consider three fundamental elements: the privacy requirements of the users, the adversary's knowledge and capabilities, and the maximal tolerated service quality degradation stemming from the obfuscation of true locations. We propose the first methodology, to the best of our knowledge, that enables a designer to find the optimal LPPM for a LBS given each user's service quality constraints against an adversary implementing the optimal inference algorithm. Such LPPM is the one that maximizes the expected distortion (error) that the optimal adversary incurs in reconstructing the actual location of a user, while fulfilling the user's service-quality requirement. We formalize the mutual optimization of user-adversary objectives (location privacy vs. correctness of localization) by using the framework of Stackelberg Bayesian games. In such setting, we develop two linear programs that output the best LPPM strategy and its corresponding optimal inference attack. Our optimal user-centric LPPM can be easily integrated in the users' mobile devices they use to access LBSs. We validate the efficacy of our game theoretic method against real location traces. Our evaluation confirms that the optimal LPPM strategy is superior to a straightforward obfuscation method, and that the optimal localization attack performs better compared to a Bayesian inference attack.