01645nas a2200169   4500008004100000022001400041245007300055210006900128300001700197490000600214520112800220100002501348700002101373700002301394700001901417856003901436       2017                        eng d  a2157-690400aA Data Mining Approach to Assess Privacy Risk in Human Mobility Data0 aData Mining Approach to Assess Privacy Risk in Human Mobility Da  a31:1–31:270 v93 aHuman mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttp://doi.acm.org/10.1145/3106774