@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}
}
