Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A model for enriching trajectories with semantic geographical information
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A conceptual view on trajectories
Data & Knowledge Engineering
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Privacy Preservation in the Publication of Trajectories
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Anonymizing moving objects: how to hide a MOB in a crowd?
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
Movement Data Anonymity through Generalization
Transactions on Data Privacy
The PROBE Framework for the Personalized Cloaking of Private Locations
Transactions on Data Privacy
Hiding Sequential and Spatiotemporal Patterns
IEEE Transactions on Knowledge and Data Engineering
Privacy preservation in the dissemination of location data
ACM SIGKDD Explorations Newsletter
Trajectory anonymity in publishing personal mobility data
ACM SIGKDD Explorations Newsletter
Preserving privacy of moving objects via temporal clustering of spatio-temporal data streams
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
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The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for analyzing and mining human mobility, but new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses even greater privacy threats w.r.t. raw geometric location data. In this paper we propose a privacy model defining the attack model of semantic trajectory linking, together with a privacy notion, called c-safety. This method provides an upper bound to the probability of inferring that a given person, observed in a sequence of nonsensitive places, has also stopped in any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on a real-life GPS trajectory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.