01842nas a2200169   4500008003900000245007300039210006900112300001100181490000600192520129800198100001901496700002201515700002001537700001801557700001901575856007801594       2011                          d00aC-safety: a framework for the anonymization of semantic trajectories0 aCsafety a framework for the anonymization of semantic trajectori  a73-1010 v43 aThe 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’s inference succeeds is much lower than the theoretical upper bound
established.1 aMonreale, Anna1 aTrasarti, Roberto1 aPedreschi, Dino1 aRenso, Chiara1 aBogorny, Vania  uhttp://dl.acm.org/citation.cfm?id=2019319&CFID=803961971&CFTOKEN=35994039