A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Protection of Location Privacy using Dummies for Location-based Services
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
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
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
Protecting Moving Trajectories with Dummies
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Towards Trajectory Anonymization: a Generalization-Based Approach
Transactions on Data Privacy
Distortion-based anonymity for continuous queries in location-based mobile services
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Movement Data Anonymity through Generalization
Transactions on Data Privacy
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
Nearest neighbor search on moving object trajectories
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Balancing trajectory privacy and data utility using a personalized anonymization model
Journal of Network and Computer Applications
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With rapid development of positioning techniques and location based services (LBS), locations and traces of moving objects are collected by service providers, the data will then be published for novel applications. Although analyzing and mining trajectories is useful for mobility-related applications, new challenges of trajectory privacy leakage arise accordingly. Trajectories contain rich spatio-temporal history information that may expose users' whereabouts and other personal privacy. At present, trajectory k-anonymity which aims at anonymizing k trajectories together on all sample points is one of the most popular techniques to protect trajectory privacy. The challenge lies in how to find trajectory k-anonymity sets. In this paper, a trajectory graph is constructed to simulate spatial relations of trajectories, based on which we propose to find trajectory k-anonymity sets through graph partition, which is proven NP-complete. We then propose a greedy partition method to find trajectory k-anonymity sets, as well as yielding low information loss. We run a series of experiments on both real-world and synthetic datasets, the results show the effectiveness of our method.