Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
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
Anonymization of moving objects databases by clustering and perturbation
Information Systems
Protecting privacy against location-based personal identification
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
Microaggregation- and permutation-based anonymization of movement data
Information Sciences: an International Journal
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The widespread deployment of technologies with tracking capabilities, like GPS, GSM, RFID and on-line social networks, allows mass collection of spatio-temporal data about their users. As a consequence, several methods aimed at anonymizing spatio-temporal data before their publication have been proposed in recent years. Such methods are based on a number of underlying privacy models. Among these models, (k,@d)-anonymity claims to extend the widely used k-anonymity concept by exploiting the spatial uncertainty @d=0 in the trajectory recording process. In this paper, we prove that, for any @d0 (that is, whenever there is actual uncertainty), (k,@d)-anonymity does not offer trajectory k-anonymity, that is, it does not hide an original trajectory in a set of k indistinguishable anonymized trajectories. Hence, the methods based on (k,@d)-anonymity, like Never Walk Alone (NWA) and Wait For Me (W4M) can offer trajectory k-anonymity only when @d=0 (no uncertainty). Thus, the idea of exploiting the recording uncertainty @d to achieve trajectory k-anonymity with information loss inversely proportional to @d turns out to be flawed.