02783nas a2200517   4500008004100000020002200041245008500063210006900148260005200217520120400269100002301473700002501496700002701521700002101548700002101569700001801590700002101608700002201629700001901651700002501670700002301695700002201718700002201740700002101762700001601783700002001799700002601819700002401845700002001869700002201889700002301911700002001934700001901954700002201973700002001995700002002015700002002035700001802055700001902073700002302092700001802115700002002133700002402153700002102177856006702198       2020                        eng d  a978-3-030-65965-300aPrediction and Explanation of Privacy Risk on Mobility Data with Neural Networks0 aPrediction and Explanation of Privacy Risk on Mobility Data with  aChambSpringer International Publishingc2020//3 aThe analysis of privacy risk for mobility data is a fundamental part of any privacy-aware process based on such data. Mobility data are highly sensitive. Therefore, the correct identification of the privacy risk before releasing the data to the public is of utmost importance. However, existing privacy risk assessment frameworks have high computational complexity. To tackle these issues, some recent work proposed a solution based on classification approaches to predict privacy risk using mobility features extracted from the data. In this paper, we propose an improvement of this approach by applying long short-term memory (LSTM) neural networks to predict the privacy risk directly from original mobility data. We empirically evaluate privacy risk on real data by applying our LSTM-based approach. Results show that our proposed method based on a LSTM network is effective in predicting the privacy risk with results in terms of F1 of up to 0.91. Moreover, to explain the predictions of our model, we employ a state-of-the-art explanation algorithm, Shap. We explore the resulting explanation, showing how it is possible to provide effective predictions while explaining them to the end-user.1 aNaretto, Francesca1 aPellungrini, Roberto1 aNardini, Franco, Maria1 aGiannotti, Fosca1 aKoprinska, Irena1 aKamp, Michael1 aAppice, Annalisa1 aLoglisci, Corrado1 aAntonie, Luiza1 aZimmermann, Albrecht1 aGuidotti, Riccardo1 aÖzgöbek, Özlem1 aRibeiro, Rita, P.1 aGavaldà, Ricard1 aGama, João1 aAdilova, Linara1 aKrishnamurthy, Yamuna1 aFerreira, Pedro, M.1 aMalerba, Donato1 aMedeiros, Ibéria1 aCeci, Michelangelo1 aManco, Giuseppe1 aMasciari, Elio1 aRas, Zbigniew, W.1 aChristen, Peter1 aNtoutsi, Eirini1 aSchubert, Erich1 aZimek, Arthur1 aMonreale, Anna1 aBiecek, Przemyslaw1 aRinzivillo, S1 aKille, Benjamin1 aLommatzsch, Andreas1 aGulla, Jon, Atle  uhttps://link.springer.com/chapter/10.1007/978-3-030-65965-3_3401581nas a2200397   4500008004100000020002200041245009100063210006900154260004400223300001400267520041400281100001400695700001600709700001700725700001400742700001500756700001600771700002100787700001900808700001700827700001500844700002100859700002000880700002300900700001600923700001800939700002100957700002400978700001901002700001601021700001901037700001801056700001901074700002101093856006901114       2018                        eng d  a978-3-319-61893-700aHow Data Mining and Machine Learning Evolved from Relational Data Base to Data Science0 aHow Data Mining and Machine Learning Evolved from Relational Dat  aChambSpringer International Publishing  a287 - 3063 aDuring the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.1 aAmato, G.1 aCandela, L.1 aCastelli, D.1 aEsuli, A.1 aFalchi, F.1 aGennaro, C.1 aGiannotti, Fosca1 aMonreale, Anna1 aNanni, Mirco1 aPagano, P.1 aPappalardo, Luca1 aPedreschi, Dino1 aPratesi, Francesca1 aRabitti, F.1 aRinzivillo, S1 aRossetti, Giulio1 aRuggieri, Salvatore1 aSebastiani, F.1 aTesconi, M.1 aFlesca, Sergio1 aGreco, Sergio1 aMasciari, Elio1 aSaccà, Domenico  uhttps://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_17