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
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Privacy Preservation in the Publication of Trajectories
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
Proceedings of the VLDB Endowment
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
Towards Trajectory Anonymization: a Generalization-Based Approach
Transactions on Data Privacy
Privacy Preserving Publication of Moving Object Data
Privacy in Location-Based Applications
Clustering Trajectories of Moving Objects in an Uncertain World
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Movement Data Anonymity through Generalization
Transactions on Data Privacy
Anonymization of moving objects databases by clustering and perturbation
Information Systems
Nearest neighbor search on moving object trajectories
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Efficient Time-Stamped Event Sequence Anonymization
ACM Transactions on the Web (TWEB)
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With the growing prevalence of location-aware devices, the amount of trajectories generated by moving objects has been dramatically increased, resulting in various novel data mining applications. Since trajectories may contain sensitive information about their moving objects, so they ought to be anonymized before making them accessible to the public. Many existing approaches for trajectory anonymization consider the same privacy level for all moving objects, whereas different moving objects may have different privacy requirements. In this paper, we propose a novel greedy clustering-based approach for anonymizing trajectory data in which the privacy requirements of moving objects are not necessarily the same. We first assign a privacy level to each trajectory based on the privacy requirement of its moving object. We then partition trajectories into a set of fixed-radius clusters based on the EDR distance. Each cluster is created such that its size is proportional to the maximum privacy level of trajectories within it. We finally anonymize trajectories of each cluster using a novel matching point algorithm. The experimental results show that our approach can achieve a satisfactory trade-off between space distortion and re-identification probability of trajectory data, which is proportional to the privacy requirement of each moving object.