01643nas a2200157   4500008003900000245007100039210006900110300001000179520109600189100001901285700002201304700001801326700002001344700001901364856010201383       2010                          d00aPreserving privacy in semantic-rich trajectories of human mobility0 aPreserving privacy in semanticrich trajectories of human mobilit  a47-543 aThe 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.1 aMonreale, Anna1 aTrasarti, Roberto1 aRenso, Chiara1 aPedreschi, Dino1 aBogorny, Vania  uhttps://kdd.isti.cnr.it/publications/preserving-privacy-semantic-rich-trajectories-human-mobility