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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Poulis, G. Skiadopoulos, S. Loukides, G. Gkoulala-Divanis, A. |
| Copyright Year | 2013 |
| Abstract | Advances in positioning technologies together with the wide adoption of GPS-enabled smartphones enable accurate and low-cost tracking of user location. This allows the collection of large amounts of person-specific mobility data that offer remarkable opportunities for data analysis. Yet, the sharing of such data poses significant privacy risks. This enunciates the need for privacy-preserving, trajectory data publishing methods. Existing approaches are either limited in their privacy specification component or they incur significant, and often unnecessary, data distortion. In response, we propose a novel framework for anonymizing trajectory data that prevents the disclosure of both identity and sensitive location information, while retaining data utility. Our framework involves: (i) selecting similar trajectories, by employing Z-ordering or data projections on frequent sub trajectories, (ii) organizing the selected trajectories into carefully constructed clusters, and (ii) anonymizing each cluster separately. We develop algorithms to realize our framework, which are effective and efficient, as verified by extensive experiments. |
| Sponsorship | Toshiba |
| Starting Page | 867 |
| Ending Page | 874 |
| File Size | 339855 |
| Page Count | 8 |
| File Format | |
| e-ISBN | 9781479931422 |
| DOI | 10.1109/ICDMW.2013.136 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-07 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Data privacy Publishing Clustering algorithms Trajectory Electronic mail Sorting |
| Content Type | Text |
| Resource Type | Article |
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