Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
A Flexible, Privacy-Preserving Authentication Framework for Ubiquitous Computing Environments
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
IEEE Transactions on Knowledge and Data Engineering
From data privacy to location privacy: models and algorithms
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
A Flexible Privacy-Enhanced Location-Based Services System Framework and Practice
IEEE Transactions on Mobile Computing
Approximate Evaluation of Range Nearest Neighbor Queries with Quality Guarantee
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Realistic radio propagation models (RPMs) for VANET simulations
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
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In pervasive computing environments, Location-Based Services (LBSs) are becoming increasingly important due to continuous advances in mobile networks and positioning technologies. Nevertheless, the wide deployment of LBSs can jeopardize the location privacy of mobile users. Consequently, providing safeguards for location privacy of mobile users against being attacked is an important research issue. In this paper a new scheme for safeguarding location privacy is proposed. Our approach supports location K-anonymity for a wide range of mobile users with their own desired anonymity levels by clustering. The whole area of all users is divided into clusters recursively in order to get the Minimum Bounding Rectangle (MBR). The exact location information of a user is replaced by his MBR. Privacy analysis shows that our approach can achieve high resilience to location privacy threats and provide more privacy than users expect. Complexity analysis shows clusters can be adjusted in real time as mobile users join or leave. Moreover, the clustering algorithms possess strong robustness.