@conference {1597,
	title = {Enhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection},
	booktitle = {Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT},
	year = {2024},
	publisher = {INSTICC},
	organization = {INSTICC},
	isbn = {978-989-758-709-2},
	doi = {10.5220/0012863600003767},
	author = {Luca Corbucci and Anna Monreale and Roberto Pellungrini}
}
@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}
}
@article {1410,
	title = {Modeling Adversarial Behavior Against Mobility Data Privacy},
	journal = {IEEE Transactions on Intelligent Transportation SystemsIEEE Transactions on Intelligent Transportation Systems},
	year = {2020},
	month = {2020},
	pages = {1 - 14},
	abstract = {Privacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way.},
	isbn = {1558-0016},
	doi = {https://doi.org/10.1109/TITS.2020.3021911},
	url = {https://ieeexplore.ieee.org/abstract/document/9199893},
	author = {Roberto Pellungrini and Luca Pappalardo and F. Simini and Anna Monreale}
}
@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}
}
@conference {1195,
	title = {Human Mobility from theory to practice: Data, Models and Applications},
	booktitle = {Companion of The 2019 World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019.},
	year = {2019},
	abstract = {The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library {\textquotedblright}scikit-mobility{\textquotedblright} developed by the presenters of the tutorial.},
	doi = {10.1145/3308560.3320099},
	url = {https://doi.org/10.1145/3308560.3320099},
	author = {Luca Pappalardo and Gianni Barlacchi and Roberto Pellungrini and Filippo Simini}
}
@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}
}
@conference {1197,
	title = {Analyzing Privacy Risk in Human Mobility Data},
	booktitle = {Software Technologies: Applications and Foundations - STAF 2018 Collocated Workshops, Toulouse, France, June 25-29, 2018, Revised Selected Papers},
	year = {2018},
	abstract = {Mobility data are of fundamental importance for understanding the patterns of human movements, developing analytical services and modeling human dynamics. Unfortunately, mobility data also contain individual sensitive information, making it necessary an accurate privacy risk assessment for the individuals involved. In this paper, we propose a methodology for assessing privacy risk in human mobility data. Given a set of individual and collective mobility features, we define the minimum data format necessary for the computation of each feature and we define a set of possible attacks on these data formats. We perform experiments computing the empirical risk in a real-world mobility dataset, and show how the distributions of the considered mobility features are affected by the removal of individuals with different levels of privacy risk.},
	doi = {10.1007/978-3-030-04771-9_10},
	url = {https://doi.org/10.1007/978-3-030-04771-9_10},
	author = {Roberto Pellungrini and Luca Pappalardo and Francesca Pratesi and Anna Monreale}
}
@conference {1198,
	title = {Assessing Privacy Risk in Retail Data},
	booktitle = {Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, PAP 2017, Held in Conjunction with ECML PKDD 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers},
	year = {2017},
	abstract = {Retail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.},
	doi = {10.1007/978-3-319-71970-2_3},
	url = {https://doi.org/10.1007/978-3-319-71970-2_3},
	author = {Roberto Pellungrini and Francesca Pratesi and Luca Pappalardo}
}
@article {1013,
	title = {A Data Mining Approach to Assess Privacy Risk in Human Mobility Data},
	journal = {ACM Trans. Intell. Syst. Technol.},
	volume = {9},
	number = {3},
	year = {2017},
	pages = {31:1{\textendash}31:27},
	abstract = {Human mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.},
	issn = {2157-6904},
	doi = {10.1145/3106774},
	url = {http://doi.acm.org/10.1145/3106774},
	author = {Roberto Pellungrini and Luca Pappalardo and Francesca Pratesi and Anna Monreale}
}
@booklet {993,
	title = {Fast Estimation of Privacy Risk in Human Mobility Data},
	year = {2017},
	abstract = {Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual{\textquoteright}s mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods. },
	isbn = {978-3-319-66283-1},
	doi = {10.1007/978-3-319-66284-8_35 },
	author = {Roberto Pellungrini and Luca Pappalardo and Francesca Pratesi and Anna Monreale}
}
