TY - CONF T1 - Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks T2 - ECML PKDD 2020 Workshops Y1 - 2020 A1 - Francesca Naretto A1 - Roberto Pellungrini A1 - Nardini, Franco Maria A1 - Fosca Giannotti ED - Koprinska, Irena ED - Kamp, Michael ED - Appice, Annalisa ED - Loglisci, Corrado ED - Antonie, Luiza ED - Zimmermann, Albrecht ED - Riccardo Guidotti ED - Özgöbek, Özlem ED - Ribeiro, Rita P. ED - Gavaldà, Ricard ED - Gama, João ED - Adilova, Linara ED - Krishnamurthy, Yamuna ED - Ferreira, Pedro M. ED - Malerba, Donato ED - Medeiros, Ibéria ED - Ceci, Michelangelo ED - Manco, Giuseppe ED - Masciari, Elio ED - Ras, Zbigniew W. ED - Christen, Peter ED - Ntoutsi, Eirini ED - Schubert, Erich ED - Zimek, Arthur ED - Anna Monreale ED - Biecek, Przemyslaw ED - S Rinzivillo ED - Kille, Benjamin ED - Lommatzsch, Andreas ED - Gulla, Jon Atle AB - The 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. JF - ECML PKDD 2020 Workshops PB - Springer International Publishing CY - Cham SN - 978-3-030-65965-3 UR - https://link.springer.com/chapter/10.1007/978-3-030-65965-3_34 ER -