The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Enabling private continuous queries for revealed user locations
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Protecting privacy against location-based personal identification
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
C-safety: a framework for the anonymization of semantic trajectories
Transactions on Data Privacy
Trajectory anonymity in publishing personal mobility data
ACM SIGKDD Explorations Newsletter
Preserving location privacy without exact locations in mobile services
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Location-based services, such as on-line maps, obtain the exact location of numerous mobile users. This information can be published for research or commercial purposes. However, privacy may be compromised if a user is in the proximity of a sensitive site (e.g., hospital). To preserve privacy, existing methods employ the K-anonymity paradigm to hide each affected user in a group that contains at least K茂戮驴 1 other users. Nevertheless, current solutions have the following drawbacks: (i)they may fail to achieve anonymity, (ii)they may cause excessive distortion of location data and (iii)they incur high computational cost.In this paper, we define formally the attack model and discuss the conditions that guarantee privacy. Then, we propose two algorithms which employ 2-D to 1-D transformations to anonymize the locations of users in the proximity of sensitive sites. The first algorithm, called MK, creates anonymous groups based on the set of user locations only, and exhibits very low computational cost. The second algorithm, called BK, performs bichromatic clustering of both user locations and sensitive sites; BK is slower but more accurate than MK. We show experimentally that our algorithms outperform the existing methods in terms of computational cost and data distortion.