TY - CONF T1 - Predicting and Explaining Privacy Risk Exposure in Mobility Data T2 - Discovery Science Y1 - 2020 A1 - Francesca Naretto A1 - Roberto Pellungrini A1 - Anna Monreale A1 - Nardini, Franco Maria A1 - Musolesi, Mirco ED - Appice, Annalisa ED - Tsoumakas, Grigorios ED - Manolopoulos, Yannis ED - Matwin, Stan AB - Mobility 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. JF - Discovery Science PB - Springer International Publishing CY - Cham SN - 978-3-030-61527-7 UR - https://link.springer.com/chapter/10.1007/978-3-030-61527-7_27 ER - 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 -