01483nas a2200145   4500008004100000020002200041245005900063210005900122520097800181100002501159700002101184700002301205700001901228856009001247       2017                        eng d  a978-3-319-66283-100aFast Estimation of Privacy Risk in Human Mobility Data0 aFast Estimation of Privacy Risk in Human Mobility Data3 aMobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual’s mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods. 1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttps://kdd.isti.cnr.it/publications/fast-estimation-privacy-risk-human-mobility-data