@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}
}
@conference {1432,
	title = {Privacy Risk for Individual Basket Patterns},
	booktitle = {ECML PKDD 2018 Workshops},
	year = {2019},
	month = {2019//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Retail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.},
	isbn = {978-3-030-13463-1},
	doi = {https://doi.org/10.1007/978-3-030-13463-1_11},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-13463-1_11},
	author = {Roberto Pellungrini and Anna Monreale and Riccardo Guidotti},
	editor = {Alzate, Carlos and Anna Monreale and Bioglio, Livio and Bitetta, Valerio and Bordino, Ilaria and Caldarelli, Guido and Ferretti, Andrea and Riccardo Guidotti and Gullo, Francesco and Pascolutti, Stefano and Pensa, Ruggero G. and Robardet, C{\'e}line and Squartini, Tiziano}
}
@article {1134,
	title = {Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	year = {2018},
	abstract = {Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer{\textquoteright}s decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer{\textquoteright}s stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.},
	doi = {10.1109/TKDE.2018.2872587},
	url = {https://ieeexplore.ieee.org/abstract/document/8477157},
	author = {Riccardo Guidotti and Giulio Rossetti and Luca Pappalardo and Fosca Giannotti and Dino Pedreschi}
}
