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L. Milli, Monreale, A., Rossetti, G., Giannotti, F., Pedreschi, D., and Sebastiani, F., Quantification Trees, in 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013, 2013, pp. 528–536.
A. Monreale, Naretto, F., and Rizzo, S., Agnostic Label-Only Membership Inference Attack, in 17th International Conference on Network and System Security, 2023.
A. Monreale, Trasarti, R., Pedreschi, D., Renso, C., and Bogorny, V., C-safety: a framework for the anonymization of semantic trajectories, Transactions on Data Privacy, vol. 4, pp. 73-101, 2011.
A. Monreale, Pinelli, F., Trasarti, R., and Giannotti, F., Location Prediction through Trajectory Pattern Mining (Extended Abstract), in SEBD, 2010, pp. 134-141.
A. Monreale, Pinelli, F., Trasarti, R., and Giannotti, F., WhereNext: a Location Predictor on Trajectory Pattern Mining, 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2009.
A. Monreale, Rinzivillo, S., Pratesi, F., Giannotti, F., and Pedreschi, D., Privacy-by-Design in Big Data Analytics and Social Mining, EPJ Data Science, vol. 10, 2014.
A. Monreale, Trasarti, R., Renso, C., Pedreschi, D., and Bogorny, V., Preserving privacy in semantic-rich trajectories of human mobility, in SPRINGL, 2010, pp. 47-54.
A. Monreale, Pedreschi, D., Pensa, R. G., and Pinelli, F., Anonymity preserving sequential pattern mining, Artif. Intell. Law, vol. 22, pp. 141–173, 2014.
A. Monreale, Wang, H. Wendy, Pratesi, F., Rinzivillo, S., Pedreschi, D., Andrienko, G., and Andrienko, N., Privacy-Preserving Distributed Movement Data Aggregation, in Geographic Information Science at the Heart of Europe, D. Vandenbroucke, Bucher, B., and Crompvoets, J., Eds. Springer International Publishing, 2013, pp. 225-245.
A. Monreale, Andrienko, G., Andrienko, N., Giannotti, F., Pedreschi, D., Rinzivillo, S., and Wrobel, S., Movement Data Anonymity through Generalization, Transactions on Data Privacy, vol. 3, pp. 91–121, 2010.
A. Monreale and Wang, H. Wendy, Privacy-Preserving Outsourcing of Data Mining, in 40th IEEE Annual Computer Software and Applications Conference, {COMPSAC} Workshops 2016, Atlanta, GA, USA, June 10-14, 2016, Atlanta, GA, USA, 2016.
D. Montesi, Renso, C., and Turini, F., Using Temporary Integrity Constraints to Optimize Databases, in FAPR, 1996, pp. 430-435.
F. Morandin, Amato, G., Gini, R., Metta, C., Parton, M., and Pascutto, G. C., SAI a Sensible Artificial Intelligence that plays Go. 2019.
D. Morelli, Rossi, A., Cairo, M., and Clifton, D. A., Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations, Sensors, vol. 19, p. 3163, 2019.
V. Morini, Pollacci, L., and Rossetti, G., Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study, Applied Sciences, vol. 11, p. 5390, 2021.
P. Mukala, Cerone, A., and Turini, F., An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities, Education and Information Technologies, vol. 22, pp. 3207–3229, 2017.
P. Mukala, Cerone, A., and Turini, F., An exploration of learning processes as process maps in FLOSS repositories. 2015.
P. Mukala, Cerone, A., and Turini, F., Mining learning processes from FLOSS mailing archives, in Conference on e-Business, e-Services and e-Society, 2015.
P. Mukala, Cerone, A., and Turini, F., Process mining event logs from FLOSS data: state of the art and perspectives, in International Conference on Software Engineering and Formal Methods, 2014.
P. Mukala, Cerone, A., and Turini, F., Ontolifloss: Ontology for learning processes in FLOSS communities, in International Conference on Software Engineering and Formal Methods, 2014.
P. Mukala, Cerone, A., and Turini, F., An abstract state machine (ASM) representation of learning process in FLOSS communities, in International Conference on Software Engineering and Formal Methods, 2014.
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M. Nanni, Raffaetà, A., Renso, C., and Turini, F., Deductive and Inductive Reasoning on Spatio-Temporal Data, in INAP/WLP, 2004, pp. 98-115.
M. Nanni, Nijssen, S., O'Sullivan, B., Paparrizou, A., Pedreschi, D., and Simonis, H., The Inductive Constraint Programming Loop, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach, vol. 10101, p. 303, 2017.
M. Nanni, Kotthoff, L., Guidotti, R., O'Sullivan, B., and Pedreschi, D., ICON Loop Carpooling Show Case, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach, vol. 10101, p. 310, 2017.
M. Nanni, Kuijpers, B., Körner, C., May, M., and Pedreschi, D., Spatiotemporal Data Mining, in Mobility, Data Mining and Privacy, 2008, pp. 267-296.

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