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A generalisation-based approach to anonymising movement data

: Andrienko, G.; Andrienko, N.; Giannotti, F.; Monreale, A.; Pedreschi, D.; Rinzivillo, S.

Postprint urn:nbn:de:0011-n-1322657 (1.7 MByte PDF)
MD5 Fingerprint: 66f52aeeb319ea3e974ace54344b397a
Erstellt am: 1.6.2010

Painho, M. ; Association of Geographic Information Laboratories in Europe -AGILE-:
13th AGILE International Conference on Geographic Information Science 2010 : 10-14 May 2010, Guimarães, Portugal
Guimarães, 2010
ISBN: 978-989-20-1953-6
10 S.
Conference on Geographic Information Science <13, 2010, Guimarães>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

The possibility to collect, store, disseminate, and analyze data about movements of people raises very serious privacy concerns, given the sensitivity of the information about personal positions. In particular, sensitive information about individuals can be uncovered with the use of data mining and visual analytics methods. In this paper we present a method for the generalization of trajectory data that can be adopted as the first step of a process to obtain k-anonymity in spatio-temporal datasets. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.