Location disclosure risks of releasing trajectory distances
| Autor: | Mehmet Ercan Nergiz, Yucel Saygin, Mehmet Emre Gursoy, Emre Kaplan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
TheoryofComputation_MISCELLANEOUS
Information Systems and Management Similarity (geometry) Vehicle tracking system business.industry Computer science Computation 02 engineering and technology Adversary computer.software_genre 020204 information systems 0202 electrical engineering electronic engineering information engineering Data analysis Trajectory 020201 artificial intelligence & image processing Data mining business Set (psychology) computer Fleet management |
| Zdroj: | Data & Knowledge Engineering. 113:43-63 |
| ISSN: | 0169-023X |
| DOI: | 10.1016/j.datak.2017.10.001 |
| Popis: | Location tracking devices enable trajectories to be collected for new services and applications such as vehicle tracking and fleet management. While trajectory data is a lucrative source for data analytics, it also contains sensitive and commercially critical information. This has led to the development of systems that enable privacy-preserving computation over trajectory databases, but many of such systems in fact (directly or indirectly) allow an adversary to compute the distance (or similarity) between two trajectories. We show that the use of such systems raises privacy concerns when the adversary has a set of known trajectories. Specifically, given a set of known trajectories and their distances to a private, unknown trajectory, we devise an attack that yields the locations which the private trajectory has visited, with high confidence. The attack can be used to disclose both positive results (i.e., the victim has visited a certain location) and negative results (i.e., the victim has not visited a certain location). Experiments on real and synthetic datasets demonstrate the accuracy of our attack. |
| Databáze: | OpenAIRE |
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