k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Replication is not needed: single database, computationally-private information retrieval
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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Proceedings of the 1st international conference on Mobile systems, applications and services
PRIVE: anonymous location-based queries in distributed mobile systems
Proceedings of the 16th international conference on World Wide Web
Alternative Algorithm for Hilbert's Space-Filling Curve
IEEE Transactions on Computers
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
IEEE Transactions on Knowledge and Data Engineering
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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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
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
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SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
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PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
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Proceedings of the 12th ACM international conference on Ubiquitous computing
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Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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ACM SIGKDD Explorations Newsletter
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Proceedings of the second ACM conference on Data and Application Security and Privacy
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Proceedings of the 2012 ACM conference on Computer and communications security
Countering overlapping rectangle privacy attack for moving kNN queries
Information Systems
Exploring dependency for query privacy protection in location-based services
Proceedings of the third ACM conference on Data and application security and privacy
K-anonymity in indoor spaces through hierarchical graphs
Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
Geo-indistinguishability: differential privacy for location-based systems
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Protecting query privacy in location-based services
Geoinformatica
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Location-based Services are emerging as popular applications in pervasive computing. Spatial k -anonymity is used in Location-based Services to protect privacy, by hiding the association of a specific query with a specific user. Unfortunately, this approach fails in many practical cases such as: (i) personalized services, where the user identity is required, or (ii) applications involving groups of users (e.g., employees of the same company); in this case, associating a query to any member of the group, violates privacy. In this paper, we introduce the concept of Location Diversity , which solves the above-mentioned problems. Location Diversity improves Spatial k -anonymity by ensuring that each query can be associated with at least *** different semantic locations (e.g., school, shop, hospital, etc). We present an attack model that maps each observed query to a linear equation involving semantic locations, and we show that a necessary condition to preserve privacy is the existence of infinite solutions in the resulting system of linear equations. Based on this observation, we develop algorithms that generate groups of semantic locations, which preserve privacy and minimize the expected query processing and communication cost. The experimental evaluation demonstrates that our approach reduces significantly the privacy threats, while incurring minimal overhead.