01574nas a2200193   4500008004100000020001400041245005500055210005400110260001600164300001100180490000800191520100500199100002301204700002301227700001801250700001901268700002101287856007201308       2020                        eng d  a0169-023X00aPRIMULE: Privacy risk mitigation for user profiles0 aPRIMULE Privacy risk mitigation for user profiles  c2020/01/01/  a1017860 v1253 aThe availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification.1 aPratesi, Francesca1 aGabrielli, Lorenzo1 aCintia, Paolo1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S0169023X18305342