@conference {1547,
	title = {Agnostic Label-Only Membership Inference Attack},
	booktitle = {17th International Conference on Network and System Security},
	year = {2023},
	publisher = {Springer},
	organization = {Springer},
	author = {Anna Monreale and Francesca Naretto and Simone Rizzo}
}
@conference {1548,
	title = {Evaluating the Privacy Exposure of Interpretable Global and Local Explainers},
	booktitle = {Submitted at Journal of Artificial Intelligence and Law},
	year = {2023},
	author = {Francesca Naretto and Anna Monreale and Fosca Giannotti}
}
@conference {1546,
	title = {EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories},
	booktitle = {Discovery Science },
	year = {2023},
	author = {Francesca Naretto and Roberto Pellungrini and Daniele Fadda and Salvo Rinzivillo}
}
@conference {1552,
	title = {Monitoring Fairness in HOLDA},
	booktitle = {HHAI 2022: Augmenting Human Intellect - Proceedings of the First International Conference on Hybrid Human-Artificial Intelligence, Amsterdam, The Netherlands, 13-17 June 2022},
	year = {2022},
	publisher = {IOS Press},
	organization = {IOS Press},
	doi = {10.3233/FAIA220205},
	url = {https://doi.org/10.3233/FAIA220205},
	author = {Michele Fontana and Francesca Naretto and Anna Monreale and Fosca Giannotti},
	editor = {Stefan Schlobach and Mar{\'\i}a P{\'e}rez-Ortiz and Myrthe Tielman}
}
@article {1476,
	title = {Benchmarking and Survey of Explanation Methods for Black Box Models},
	journal = {CoRR},
	volume = {abs/2102.13076},
	year = {2021},
	url = {https://arxiv.org/abs/2102.13076},
	author = {Francesco Bodria and Fosca Giannotti and Riccardo Guidotti and Francesca Naretto and Dino Pedreschi and S Rinzivillo}
}
@conference {1551,
	title = {Explainable for Trustworthy AI},
	booktitle = {Human-Centered Artificial Intelligence - Advanced Lectures, 18th European Advanced Course on AI, ACAI 2021, Berlin, Germany, October 11-15, 2021, extended and improved lecture notes},
	year = {2021},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-031-24349-3_10},
	url = {https://doi.org/10.1007/978-3-031-24349-3_10},
	author = {Fosca Giannotti and Francesca Naretto and Francesco Bodria}
}
@conference {1478,
	title = {A new approach for cross-silo federated learning and its privacy risks},
	booktitle = {18th International Conference on Privacy, Security and Trust, PST 2021, Auckland, New Zealand, December 13-15, 2021},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	doi = {10.1109/PST52912.2021.9647753},
	url = {https://doi.org/10.1109/PST52912.2021.9647753},
	author = {Michele Fontana and Francesca Naretto and Anna Monreale}
}
@conference {1477,
	title = {Privacy Risk Assessment of Individual Psychometric Profiles},
	booktitle = {Discovery Science - 24th International Conference, DS 2021, Halifax, NS, Canada, October 11-13, 2021, Proceedings},
	year = {2021},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-030-88942-5_32},
	url = {https://doi.org/10.1007/978-3-030-88942-5_32},
	author = {Giacomo Mariani and Anna Monreale and Francesca Naretto},
	editor = {Carlos Soares and Lu{\'\i}s Torgo}
}
@conference {1411,
	title = {Predicting and Explaining Privacy Risk Exposure in Mobility Data},
	booktitle = {Discovery Science},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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{\textquoteright}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.},
	isbn = {978-3-030-61527-7},
	doi = {https://doi.org/10.1007/978-3-030-61527-7_27},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-61527-7_27},
	author = {Francesca Naretto and Roberto Pellungrini and Anna Monreale and Nardini, Franco Maria and Musolesi, Mirco},
	editor = {Appice, Annalisa and Tsoumakas, Grigorios and Manolopoulos, Yannis and Matwin, Stan}
}
@conference {1430,
	title = {Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks},
	booktitle = {ECML PKDD 2020 Workshops},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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.},
	isbn = {978-3-030-65965-3},
	doi = {https://doi.org/10.1007/978-3-030-65965-3_34},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-65965-3_34},
	author = {Francesca Naretto and Roberto Pellungrini and Nardini, Franco Maria and Fosca Giannotti},
	editor = {Koprinska, Irena and Kamp, Michael and Appice, Annalisa and Loglisci, Corrado and Antonie, Luiza and Zimmermann, Albrecht and Riccardo Guidotti and {\"O}zg{\"o}bek, {\"O}zlem and Ribeiro, Rita P. and Gavald{\`a}, Ricard and Gama, Jo{\~a}o and Adilova, Linara and Krishnamurthy, Yamuna and Ferreira, Pedro M. and Malerba, Donato and Medeiros, Ib{\'e}ria and Ceci, Michelangelo and Manco, Giuseppe and Masciari, Elio and Ras, Zbigniew W. and Christen, Peter and Ntoutsi, Eirini and Schubert, Erich and Zimek, Arthur and Anna Monreale and Biecek, Przemyslaw and S Rinzivillo and Kille, Benjamin and Lommatzsch, Andreas and Gulla, Jon Atle}
}
