TY - JOUR T1 - C-safety: a framework for the anonymization of semantic trajectories JF - Transactions on Data Privacy Y1 - 2011 A1 - Anna Monreale A1 - Roberto Trasarti A1 - Dino Pedreschi A1 - Chiara Renso A1 - Vania Bogorny AB - 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’s inference succeeds is much lower than the theoretical upper bound established. VL - 4 UR - http://dl.acm.org/citation.cfm?id=2019319&CFID=803961971&CFTOKEN=35994039 ER -