k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Mix Zones: User Privacy in Location-aware Services
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Proceedings of the 1st international conference on Mobile systems, applications and services
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Anonymity for continuous data publishing
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Dynamic anonymization: accurate statistical analysis with privacy preservation
Proceedings of the 2008 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
Continuous privacy preserving publishing of data streams
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
Movement Data Anonymity through Generalization
Transactions on Data Privacy
Trajectory privacy in location-based services and data publication
ACM SIGKDD Explorations Newsletter
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This paper proposes a continuous anonymization method for a trajectory stream. In today's mobile environment, positions of moving objects are frequently sensed and collected. For real-time movement pattern analyses of people and automobiles, trajectory streams have attracted a lot of attention. Trajectory streams lead to sensitive locations, such as homes and personal hospitals. Additionally, a set of spatio-temporal data might identify a user from a trajectory stream. Therefore, publishing original trajectory streams may cause critical breaches of privacy. To protect privacy of users, we need a mechanism which makes it difficult to identify users from crowds of trajectory streams. Several techniques for anonymizing trajectories have been proposed. Anonymized trajectories can be published without concerning about privacy issues. However, for the continuous publishing of trajectory streams, existing trajectory anonymization methods are not suitable because they anonymize the overall trajectories at a time. If the existing methods are applied in the continuous publishing, the resolution of anonymized trajectory is hugely degraded or trace-ability is lost. In this paper, we propose an anonymization technique for a trajectory stream. The method continuously anonymizes trajectory streams one by one, and dynamically reforms anonymized trajectory streams to improve the resolution. The experiments showed that our method could keep the resolution at a constant level.