Min-wise independent permutations (extended abstract)
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
LeZi-update: an information-theoretic approach to track mobile users in PCS networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Learning and inferring transportation routines
Artificial Intelligence
Identification via location-profiling in GSM networks
Proceedings of the 7th ACM workshop on Privacy in the electronic society
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 publication of trajectories using microaggregation
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
Route classification using cellular handoff patterns
Proceedings of the 13th international conference on Ubiquitous computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Mobility detection using everyday GSM traces
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
A probabilistic approach to mining mobile phone data sequences
Personal and Ubiquitous Computing
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A probabilistic method for inferring common routes from mobile communication network traffic data is presented. Besides providing mobility information, valuable in a multitude of application areas, the method has the dual purpose of enabling efficient coarse-graining as well as anonymisation by mapping individual sequences onto common routes. The approach is to represent spatial trajectories by Cell ID sequences that are grouped into routes using locality-sensitive hashing and graph clustering. The method is demonstrated to be scalable, and to accurately group sequences using an evaluation set of GPS tagged data.