@article {531,
	title = {CONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects },
	journal = {Transaction in GIS},
	year = {2013},
	author = {Vania Bogorny and Chiara Renso and Artur Ribeiro de Aquino and Fernando de Lucca Siqueira and Luis Otavio Alvares}
}
@article {525,
	title = {Semantic Trajectories Modeling and Analysis},
	journal = {ACM Computing Surveys},
	volume = {45},
	number = {4},
	year = {2013},
	month = {August 2013},
	author = {Christine Parent and Stefano Spaccapietra and Chiara Renso and Gennady Andrienko and Natalia Andrienko and Vania Bogorny and Damiani M L, and Gkoulalas-Divanis A, and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Nikos Pelekis}
}
@conference {daytag2013,
	title = {Where Have You Been Today? Annotating Trajectories with DayTag},
	booktitle = {International Conference on Spatial and Spatio-temporal Databases (SSTD)},
	year = {2013},
	pages = {467-471},
	doi = {http://dx.doi.org/10.1007/978-3-642-40235-7_30},
	author = {S Rinzivillo and Fernando de Lucca Siqueira and Lorenzo Gabrielli and Chiara Renso and Vania Bogorny}
}
@article {MonrealeTPRB11,
	title = {C-safety: a framework for the anonymization of semantic trajectories},
	journal = {Transactions on Data Privacy},
	volume = {4},
	number = {2},
	year = {2011},
	pages = {73-101},
	abstract = {The increasing abundance of data about the trajectories of personal movement is opening
new opportunities for analyzing and mining human mobility. However, new risks emerge since it
opens new ways of intruding into personal privacy. Representing the personal movements as sequences
of places visited by a person during her/his movements - semantic trajectory - poses great
privacy threats. In this paper we propose a privacy model defining the attack model of semantic trajectory
linking and a privacy notion, called c-safety based on a generalization of visited places based
on a taxonomy. This method provides an upper bound to the probability of inferring that a given
person, observed in a sequence of non-sensitive places, has also visited any sensitive location. Coherently
with the privacy model, we propose an algorithm for transforming any dataset of semantic
trajectories into a c-safe one. We report a study on two real-life GPS trajectory datasets to show how
our algorithm preserves interesting quality/utility measures of the original trajectories, when mining
semantic trajectories sequential pattern mining results. We also empirically measure how the
probability that the attacker{\textquoteright}s inference succeeds is much lower than the theoretical upper bound
established.},
	url = {http://dl.acm.org/citation.cfm?id=2019319\&CFID=803961971\&CFTOKEN=35994039},
	author = {Anna Monreale and Roberto Trasarti and Dino Pedreschi and Chiara Renso and Vania Bogorny}
}
@conference {MonrealeTRPB10,
	title = {Preserving privacy in semantic-rich trajectories of human mobility},
	booktitle = {SPRINGL},
	year = {2010},
	pages = {47-54},
	abstract = {The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for analyzing and mining human mobility, but new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses even greater privacy threats w.r.t. raw geometric location data. In this paper we propose a privacy model defining the attack model of semantic trajectory linking, together with a privacy notion, called c-safety. This method provides an upper bound to the probability of inferring that a given person, observed in a sequence of nonsensitive places, has also stopped in any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on a real-life GPS trajectory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.},
	doi = {10.1145/1868470.1868481},
	author = {Anna Monreale and Roberto Trasarti and Chiara Renso and Dino Pedreschi and Vania Bogorny}
}
@inbook {RTBKKM08,
	title = {Knowledge Discovery from Geographical Data},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {243-265},
	author = {S Rinzivillo and Franco Turini and Vania Bogorny and Christine K{\"o}rner and Bart Kuijpers and Michael May}
}
