The String-to-String Correction Problem
Journal of the ACM (JACM)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Protecting Location Privacy Through Path Confusion
SECURECOMM '05 Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks
Preserving privacy in gps traces via uncertainty-aware path cloaking
Proceedings of the 14th ACM conference on Computer and communications security
Hiding Sensitive Trajectory Patterns
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
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
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy-Preserving Data Mining on Moving Object Trajectories
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Privacy in Location-Based Services: State-of-the-Art and Research Directions
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Towards Trajectory Anonymization: a Generalization-Based Approach
Transactions on Data Privacy
Protecting privacy against location-based personal identification
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
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We are experiencing the expanding use of location-based services such as ATST TeleNav GPS Navigator and Intel’s Thing Finder. Existing location-based services have collected a large amount of location data, which have great potential for statistical usage in applications like traffic flow analysis, infrastructure planning and advertisement dissemination. The key challenge is how to wisely use the data without violating each user’s location privacy concerns. In this paper, we first identify a new privacy problem, namely inference route problem, and then present our anonymization algorithms for privacy-preserving trajectory publishing. The experimental results have shown that our approach outperforms the latest related work in terms of both efficiency and effectiveness.