Location Privacy in Pervasive Computing
IEEE Pervasive Computing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Anonymity in Location-Based Services: Towards a General Framework
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Realistic Driving Trips For Location Privacy
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
PAD: privacy-area aware, dummy-based location privacy in mobile services
Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
A distortion-based metric for location privacy
Proceedings of the 8th ACM workshop on Privacy in the electronic society
Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
A user location anonymization method for location based services in a real environment
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Protecting location privacy using location semantics
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A formal model of obfuscation and negotiation for location privacy
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
A dummy-based anonymization method based on user trajectory with pauses
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Due to the popularization of location based services (LBSs), preserving user's location privacy has become a significant issue. In this paper, we assume that users are traveling by car and propose a location anonymization method which mainly aims at reducing traceability of user's movement trajectory. Since the movement patterns of car-driving users have some special characteristics due to various factors such as traffic rules, generating dummies in an artificially-synthesized manner is not practical. Therefore, in our method, we use real car trace data which were obtained from probe cars to generate dummies so that the generated dummies look like real users. In doing so, our method tries to find real traces which can cross with the user or another dummy at an intersection in order to reduce the user's traceability. Through simulations, we confirm that our method can reduce the traceability of the user's trajectory compared with some naive approaches.