02034nas a2200217   4500008004100000020002200041245006900063210006900132260005200201520129400253100002301547700002501570700001901595700002701614700002001641700002101661700002501682700002501707700001701732856006701749       2020                        eng d  a978-3-030-61527-700aPredicting and Explaining Privacy Risk Exposure in Mobility Data0 aPredicting and Explaining Privacy Risk Exposure in Mobility Data  aChambSpringer International Publishingc2020//3 aMobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people’s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task.1 aNaretto, Francesca1 aPellungrini, Roberto1 aMonreale, Anna1 aNardini, Franco, Maria1 aMusolesi, Mirco1 aAppice, Annalisa1 aTsoumakas, Grigorios1 aManolopoulos, Yannis1 aMatwin, Stan  uhttps://link.springer.com/chapter/10.1007/978-3-030-61527-7_27