01950nas a2200181   4500008004100000245008800041210006900129260001200198490000700210520137400217100002301591700001901614700002201633700002101655700002001676700002401696856004801720       2018                        eng d00aPRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems0 aPRUDEnce a system for assessing privacy risk vs utility in data   c08/20180 v113 aData describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people’s whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data.1 aPratesi, Francesca1 aMonreale, Anna1 aTrasarti, Roberto1 aGiannotti, Fosca1 aPedreschi, Dino1 aYanagihara, Tadashi  uhttp://www.tdp.cat/issues16/tdp.a284a17.pdf