Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Private queries in location based services: anonymizers are not necessary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Feeling-based location privacy protection for location-based services
Proceedings of the 16th ACM conference on Computer and communications security
Privacy preserving group nearest neighbor queries
Proceedings of the 13th International Conference on Extending Database Technology
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Louis, Lester and Pierre: three protocols for location privacy
PET'07 Proceedings of the 7th international conference on Privacy enhancing technologies
Privacy in mobile computing for location-sharing-based services
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
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In mobile object database systems, both query issuers and queried objects are subject to location privacy intrusion. One solution to this problem is to have users reduce their location resolution when making location update. Such location cloaking allows mobile objects to achieve a desired level of protection, but may not produce accurate query results. Alternatively, one can apply cryptography techniques such as secure multiparty computation to compute the spatial relationship among mobile objects without having mobile objects to disclose their location at all. This strategy produces high quality query results, but in general are computation-intensive, especially when a large number of mobile objects are involved. In this paper, we present a hybrid approach that mitigates the above dilemma. Our idea is to compute approximate query results based on cloaked location information and then refine query results by applying homomorphic encryption. We demonstrate that this approach can be used for efficient and privacy-preserving processing of KNN queries and evaluate its performance through simulation.