Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Location Privacy in Mobile Systems: A Personalized Anonymization Model
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
Similarity Search in Trajectory Databases
TIME '07 Proceedings of the 14th International Symposium on Temporal Representation and Reasoning
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
Towards trajectory anonymization: a generalization-based approach
SPRINGL '08 Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS
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
Searching for similar trajectories in spatial networks
Journal of Systems and Software
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
Quality aware privacy protection for location-based services
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Ensuring Privacy and Security for LBS through Trajectory Partitioning
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
History trajectory privacy-preserving through graph partition
Proceedings of the 1st international workshop on Mobile location-based service
Microaggregation- and permutation-based anonymization of movement data
Information Sciences: an International Journal
You can walk alone: trajectory privacy-preserving through significant stays protection
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Real-time GPS track simplification algorithm for outdoor navigation of visually impaired
Journal of Network and Computer Applications
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With the widespread use of location-based services (LBS), the number of trajectories gathered by location service providers is dynamically growing. On the one hand, mining and analyzing these spatiotemporal trajectories can help to work out a mobile-related strategic planning; on the other hand, knowledge of each trajectory can be used by adversaries to identify the user's sensitive information and lead to an unpredictable harm. The concept of trajectory k-anonymity extends from location k-anonymity that has been widely used to address this issue. The main challenge of trajectory k-anonymity is the selection of a trajectory k-anonymity set. However, existing anonymity methods ignore the trajectory similarity and direction, assuming that it has little impact on privacy. Thus, it cannot provide a preferable trajectory k-anonymity set. In this paper, we propose to use trajectory angle to evaluate trajectory similarity and direction, and construct an anonymity region on the basis of trajectory distance. Considering the various preference settings on the proportion of trajectory privacy and data utility in different scenarios, we propose a personalized anonymization model to select the trajectory k-anonymity set. Experiment results prove that our method can provide an effective trajectory k-anonymity set under various proportions of trajectory privacy and data utility requirements, while the efficiency just reduces a little.