A generalisation-based approach to anonymising movement data
Andrienko G., Andrienko N., Giannotti F., Monreale A., Pedreschi D., Rinzivillo S.
Database Applications Public Policy Issues Privacy Clustering Spatio-temporal Clustering
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.
Source: The 13th AGILE conference on Geographic Information Science, Guimarães, Portugal, 10-14 May 2010
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@inproceedings{oai:it.cnr:prodotti:92071,
title = {A generalisation-based approach to anonymising movement data},
author = {Andrienko G. and Andrienko N. and Giannotti F. and Monreale A. and Pedreschi D. and Rinzivillo S.},
booktitle = {The 13th AGILE conference on Geographic Information Science, Guimarães, Portugal, 10-14 May 2010},
year = {2010}
}
0000-0003-3099-3835