Privacy Preservation in the Publication of Trajectories
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Protecting Privacy in Continuous Location-Tracking Applications
IEEE Security and Privacy
Privacy: preserving trajectory collection
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
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
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
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
Movement Data Anonymity through Generalization
Transactions on Data Privacy
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
Publishing trajectories with differential privacy guarantees
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Efficient Time-Stamped Event Sequence Anonymization
ACM Transactions on the Web (TWEB)
Balancing trajectory privacy and data utility using a personalized anonymization model
Journal of Network and Computer Applications
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Publication of moving objects' everyday life trajectories may cause serious personal privacy leakage. Existing trajectory privacy-preserving methods try to anonymize k whole trajectories together, which may result in complicated algorithms and extra information loss. We observe that, background information are more relevant to where the moving objects really visit rather than where they just pass by. In this paper, we propose an approach called You Can Walk Alone (YCWA) to protect trajectory privacy through generalization of stay points on trajectories. By protecting stay points, sensitive information is protected, while the probability of whole trajectories' exposure is reduced. Moreover, the information loss caused by the privacy-preserving process is reduced. To the best of our knowledge, this is the first research that protects trajectory privacy through protecting significant stays or similar concepts. At last, we conduct a set of comparative experimental study on real-world dataset, the results show advantages of our approach.