@article {526,
	title = {Scalable Analysis of Movement Data for Extracting and Exploring Significant Places},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {19},
	number = {7},
	year = {2013},
	chapter = {49},
	author = {Gennady Andrienko and Natalia Andrienko and C. Hunter and S Rinzivillo and Stefan Wrobel}
}
@proceedings {358,
	title = {From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data},
	year = {2011},
	author = {Gennady Andrienko and Natalia Andrienko and Cristophe Hurter and S Rinzivillo and Stefan Wrobel}
}
@article {572,
	title = {Movement Data Anonymity through Generalization},
	journal = {Transactions on Data Privacy},
	volume = {3},
	number = {2},
	year = {2010},
	pages = {91{\textendash}121},
	abstract = {Wireless networks and mobile devices, such as mobile phones and GPS receivers, sense
and track the movements of people and vehicles, producing society-wide mobility databases. This is
a challenging scenario for data analysis and mining. On the one hand, exciting opportunities arise out
of discovering new knowledge about human mobile behavior, and thus fuel intelligent info-mobility
applications. On other hand, new privacy concerns arise when mobility data are published. The
risk is particularly high for GPS trajectories, which represent movement of a very high precision and
spatio-temporal resolution: the de-identification of such trajectories (i.e., forgetting the ID of their
associated owners) is only a weak protection, as generally it is possible to re-identify a person by observing
her routine movements. In this paper we propose a method for achieving true anonymity in
a dataset of published trajectories, by defining a transformation of the original GPS trajectories based
on spatial generalization and k-anonymity. The proposed method offers a formal data protection
safeguard, quantified as a theoretical upper bound to the probability of re-identification. We conduct
a thorough study on a real-life GPS trajectory dataset, and provide strong empirical evidence that
the proposed anonymity techniques achieve the conflicting goals of data utility and data privacy. In
practice, the achieved anonymity protection is much stronger than the theoretical worst case, while
the quality of the cluster analysis on the trajectory data is preserved.},
	url = {http://www.tdp.cat/issues/abs.a045a10.php},
	author = {Anna Monreale and Gennady Andrienko and Natalia Andrienko and Fosca Giannotti and Dino Pedreschi and S Rinzivillo and Stefan Wrobel}
}
