LeZi-update: an information-theoretic framework for personal mobility tracking in PCS networks
Wireless Networks - Selected Papers from Mobicom'99
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
A study on the value of location privacy
Proceedings of the 5th ACM workshop on Privacy in electronic society
On the Optimal Placement of Mix Zones
PETS '09 Proceedings of the 9th International Symposium on Privacy Enhancing Technologies
Proceedings of the 12th ACM international conference on Ubiquitous computing
Anonymization of location data does not work: a large-scale measurement study
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Quantifying location privacy: the case of sporadic location exposure
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
Assessing location privacy in mobile communication networks
ISC'11 Proceedings of the 14th international conference on Information security
Evaluating the privacy risk of location-based services
FC'11 Proceedings of the 15th international conference on Financial Cryptography and Data Security
Scalable mining of common routes in mobile communication network traffic data
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Privacy in mobile technology for personal healthcare
ACM Computing Surveys (CSUR)
Protecting location privacy: optimal strategy against localization attacks
Proceedings of the 2012 ACM conference on Computer and communications security
Time-clustering-based place prediction for wireless subscribers
IEEE/ACM Transactions on Networking (TON)
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As devices move within a cellular network, they register their new location with cell base stations to allow for the correct forwarding of data. We show it is possible to identify a mobile user from these records and a pre-existing location profile, based on previous movement. Two different identification processes are studied, and their performances are evaluated on real cell location traces. The best of those allows for the identification of around 80% of users. We also study the misidentified users and characterise them using hierarchical clustering techniques. Our findings highlight the difficulty of anonymizing location data, and firmly establish they are personally identifiable.