SIGMOD '95 Proceedings of the 1995 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
Location Privacy in Pervasive Computing
IEEE Pervasive Computing
Cover story: they know where you are
IEEE Spectrum
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
PRIVE: anonymous location-based queries in distributed mobile systems
Proceedings of the 16th international conference on World Wide Web
Privacy-aware location sensor networks
HOTOS'03 Proceedings of the 9th conference on Hot Topics in Operating Systems - Volume 9
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
IEEE Transactions on Knowledge and Data Engineering
Private queries in location based services: anonymizers are not necessary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy-aware mobile services over road networks
Proceedings of the VLDB Endowment
Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Privacy-preserving matching of spatial datasets with protection against background knowledge
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Nearest neighbor search with strong location privacy
Proceedings of the VLDB Endowment
Protecting privacy against location-based personal identification
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
Preserving user location privacy in mobile data management infrastructures
PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
Polylogarithmic private approximations and efficient matching
TCC'06 Proceedings of the Third conference on Theory of Cryptography
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Location privacy receives considerable attentions in emerging location based services. Most current practice however fails to incorporate users' preferences. In this paper, we propose a privacy protection solution to allow users' preferences in the fundamental query of k nearest neighbors. Particularly, users are permitted to choose privacy preferences by specifying minimum inferred region. By leveraging Hilbert curve based transformation, the additional workload from users' preferences is alleviated. What's more, this transformation reduces time-expensive region queries in two dimensional space to range ones in one dimensional space. Therefore, the time efficiency, as well as communication efficiency, is greatly improved due to clustering properties of Hilbert curve. The empirical studies demonstrate our implementation delivers both flexibility for users' preferences and scalability for time and communication costs.