@conference {1596,
	title = {PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning},
	year = {2024},
	url = {https://arxiv.org/abs/2407.15224},
	author = {Luca Corbucci and Mikko A Heikkila and David Solans Noguero and Anna Monreale and Nicolas Kourtellis}
}
@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}
}
@article {1421,
	title = {PRIMULE: Privacy risk mitigation for user profiles},
	volume = {125},
	year = {2020},
	month = {2020/01/01/},
	pages = {101786},
	abstract = {The availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et~al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et~al., 2013), a data mining tool for city users classification.},
	isbn = {0169-023X},
	doi = {https://doi.org/10.1016/j.datak.2019.101786},
	url = {https://www.sciencedirect.com/science/article/pii/S0169023X18305342},
	author = {Francesca Pratesi and Lorenzo Gabrielli and Paolo Cintia and Anna Monreale and Fosca Giannotti}
}
@article {1294,
	title = {PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach},
	journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},
	volume = {10},
	number = {5},
	year = {2019},
	pages = {1{\textendash}27},
	abstract = {The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players{\textquoteright} evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank{\textemdash}i.e. searching players and player versatility{\textemdash}showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.},
	doi = {10.1145/3343172},
	url = {https://dl.acm.org/doi/abs/10.1145/3343172},
	author = {Luca Pappalardo and Paolo Cintia and Ferragina, Paolo and Massucco, Emanuele and Dino Pedreschi and Fosca Giannotti}
}
@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 {1266,
	title = {A public data set of spatio-temporal match events in soccer competitions},
	journal = {Scientific data},
	volume = {6},
	number = {1},
	year = {2019},
	pages = {1{\textendash}15},
	abstract = {Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.},
	doi = {10.1038/s41597-019-0247-7},
	url = {https://www.nature.com/articles/s41597-019-0247-7},
	author = {Luca Pappalardo and Paolo Cintia and Alessio Rossi and Massucco, Emanuele and Ferragina, Paolo and Dino Pedreschi and Fosca Giannotti}
}
@article {1217,
	title = {Public opinion and Algorithmic bias},
	journal = {ERCIM News},
	number = {116},
	year = {2019},
	url = {https://ercim-news.ercim.eu/en116/special/public-opinion-and-algorithmic-bias},
	author = {Alina Sirbu and Fosca Giannotti and Dino Pedreschi and Kert{\'e}sz, J{\'a}nos}
}
@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}
}
@article {1138,
	title = {PRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems},
	journal = {Transactions on Data Privacy},
	volume = {11},
	number = {2},
	year = {2018},
	month = {08/2018},
	abstract = {Data describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people{\textquoteright}s whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data.},
	url = {http://www.tdp.cat/issues16/tdp.a284a17.pdf},
	author = {Francesca Pratesi and Anna Monreale and Roberto Trasarti and Fosca Giannotti and Dino Pedreschi and Yanagihara, Tadashi}
}
@conference {1049,
	title = {Privacy Preserving Multidimensional Profiling},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Recently, big data had become central in the analysis of human behavior and the development of innovative services. In particular, a new class of services is emerging, taking advantage of different sources of data, in order to consider the multiple aspects of human beings. Unfortunately, these data can lead to re-identification problems and other privacy leaks, as diffusely reported in both scientific literature and media. The risk is even more pressing if multiple sources of data are linked together since a potential adversary could know information related to each dataset. For this reason, it is necessary to evaluate accurately and mitigate the individual privacy risk before releasing personal data. In this paper, we propose a methodology for the first task, i.e., assessing privacy risk, in a multidimensional scenario, defining some possible privacy attacks and simulating them using real-world datasets.},
	doi = {10.1007/978-3-319-76111-4_15},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_15},
	author = {Francesca Pratesi and Anna Monreale and Fosca Giannotti and Dino Pedreschi}
}
@inbook {877,
	title = {Partition-Based Clustering Using Constraint Optimization},
	booktitle = {Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach},
	year = {2016},
	pages = {282{\textendash}299},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Partition-based clustering is the task of partitioning a dataset in a number of groups of examples, such that examples in each group are similar to each other. Many criteria for what constitutes a good clustering have been identified in the literature; furthermore, the use of additional constraints to find more useful clusterings has been proposed. In this chapter, it will be shown that most of these clustering tasks can be formalized using optimization criteria and constraints. We demonstrate how a range of clustering tasks can be modelled in generic constraint programming languages with these constraints and optimization criteria. Using the constraint-based modeling approach we also relate the DBSCAN method for density-based clustering to the label propagation technique for community discovery.},
	doi = {10.1007/978-3-319-50137-6_11},
	url = {http://dx.doi.org/10.1007/978-3-319-50137-6_11},
	author = {Valerio Grossi and Tias Guns and Anna Monreale and Mirco Nanni and Siegfried Nijssen}
}
@proceedings {806,
	title = {Power Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer},
	volume = {LNCS 9833},
	year = {2016},
	publisher = {Springer LNCS},
	address = {Grenoble, France},
	abstract = {Power consumption is a major obstacle for High Performance Computing (HPC) systems in their quest towards the holy grail of ExaFLOP performance. Significant advances in power efficiency have to be made before this goal can be attained and accurate modeling is an essential step towards power efficiency by optimizing system operating parameters to match dynamic energy needs. In this paper we present a study of power consumption by jobs in Eurora, a hybrid CPU-GPU-MIC system installed at the largest Italian data center. Using data from a dedicated monitoring framework, we build a data-driven model of power consumption for each user in the system and use it to predict the power requirements of future jobs. We are able to achieve good prediction results for over 80 \% of the users in the system. For the remaining users, we identify possible reasons why prediction performance is not as good. Possible applications for our predictive modeling results include scheduling optimization, power-aware billing and system-scale power modeling. All the scripts used for the study have been made available on GitHub.},
	doi = {10.1007/978-3-319-43659-3_9},
	url = {http://arxiv.org/abs/1601.05961},
	author = {Alina Sirbu and Ozalp Babaoglu}
}
@conference {864,
	title = {Predicting System-level Power for a Hybrid Supercomputer},
	booktitle = {2016 International Conference on High Performance Computing Simulation (HPCS)},
	year = {2016},
	month = {07/2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Innsbruck, Austria},
	abstract = {For current High Performance Computing systems to scale towards the holy grail of ExaFLOP performance, their power consumption has to be reduced by at least one order of magnitude. This goal can be achieved only through a combination of hardware and software advances. Being able to model and accurately predict the power consumption of large computational systems is necessary for software-level innovations such as proactive and power-aware scheduling, resource allocation and fault tolerance techniques. In this paper we present a 2-layer model of power consumption for a hybrid supercomputer (which held the top spot of the Green500 list on July 2013) that combines CPU, GPU and MIC technologies to achieve higher energy efficiency. Our model takes as input workload information - the number and location of resources that are used by each job at a certain time - and calculates the resulting system-level power consumption. When jobs are submitted to the system, the workload configuration can be foreseen based on the scheduler policies, and our model can then be applied to predict the ensuing system-level power consumption. Additionally, alternative workload configurations can be evaluated from a power perspective and more efficient ones can be selected. Applications of the model include not only power-aware scheduling but also prediction of anomalous behavior.},
	doi = {10.1109/HPCSim.2016.7568420},
	url = {http://ieeexplore.ieee.org/document/7568420/},
	author = {Alina Sirbu and Ozalp Babaoglu}
}
@conference {876,
	title = {Privacy-Preserving Outsourcing of Data Mining},
	booktitle = {40th IEEE Annual Computer Software and Applications Conference, {COMPSAC} Workshops 2016, Atlanta, GA, USA, June 10-14, 2016},
	year = {2016},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = { Atlanta, GA, USA},
	abstract = {Data mining is gaining momentum in society due to the ever increasing availability of large amounts of data, easily gathered by a variety of collection technologies and stored via computer systems. Due to the limited computational resources of data owners and the developments in cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service (DMaaS). In this paradigm, a company (data owner) lacking in expertise or computational resources outsources its mining needs to a third party service provider (server). Given the fact that the server may not be fully trusted, one of the main concerns of the DMaaS paradigm is the protection of data privacy. In this paper, we provide an overview of a variety of techniques and approaches that address the privacy issues of the DMaaS paradigm.},
	doi = {10.1109/COMPSAC.2016.169},
	url = {http://dx.doi.org/10.1109/COMPSAC.2016.169},
	author = {Anna Monreale and Hui Wendy Wang}
}
@conference {989,
	title = {Privacy-Preserving Outsourcing of Pattern Mining of Event-Log Data-A Use-Case from Process Industry},
	booktitle = {Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {With the advent of cloud computing and its model for IT services based on the Internet and big data centers, the interest of industries into XaaS ("Anything as a Service") paradigm is increasing. Business intelligence and knowledge discovery services are typical services that companies tend to externalize on the cloud, due to their data intensive nature and the algorithms complexity. What is appealing for a company is to rely on external expertise and infrastructure to compute the analytical results and models which are required by the business analysts for understanding the business phenomena under observation. Although it is advantageous to achieve sophisticated analysis there exist several serious privacy issues in this paradigm. In this paper we investigate through an industrial use-case the application of a framework for privacypreserving outsourcing of pattern mining on event-log data. Moreover, we present and discuss some ideas about possible extensions.},
	doi = {10.1109/CloudCom.2016.0095},
	author = {Marrella, Alessandro and Anna Monreale and Kloepper, Benjamin and Krueger, Martin W}
}
@article {794,
	title = {Participatory Patterns in an International Air Quality Monitoring Initiative.},
	journal = {PLoS One},
	volume = {10},
	year = {2015},
	month = {2015},
	pages = {e0136763},
	abstract = {<p>The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.</p>},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0136763},
	author = {Alina Sirbu and Becker, Martin and Saverio Caminiti and De Baets, Bernard and Elen, Bart and Francis, Louise and Pietro Gravino and Hotho, Andreas and Ingarra, Stefano and Vittorio Loreto and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Vito D P Servedio and Stumme, Gerd and Theunis, Jan and Francesca Tria and Van den Bossche, Joris}
}
@article {752,
	title = {Product assortment and customer mobility},
	journal = {EPJ Data Science},
	volume = {4},
	number = {1},
	year = {2015},
	month = {10-2015},
	pages = {1{\textendash}18},
	abstract = {Customers mobility is dependent on the sophistication of their needs: sophisticated customers need to travel more to fulfill their needs. In this paper, we provide more detailed evidence of this phenomenon, providing an empirical validation of the Central Place Theory. For each customer, we detect what is her favorite shop, where she purchases most products. We can study the relationship between the favorite shop and the closest one, by recording the influence of the shop{\textquoteright}s size and the customer{\textquoteright}s sophistication in the discordance cases, i.e. the cases in which the favorite shop is not the closest one. We show that larger shops are able to retain most of their closest customers and they are able to catch large portions of customers from smaller shops around them. We connect this observation with the shop{\textquoteright}s larger sophistication, and not with its other characteristics, as the phenomenon is especially noticeable when customers want to satisfy their sophisticated needs. This is a confirmation of the recent extensions of the Central Place Theory, where the original assumptions of homogeneity in customer purchase power and needs are challenged. Different types of shops have also different survival logics. The largest shops get closed if they are unable to catch customers from the smaller shops, while medium size shops get closed if they cannot retain their closest customers. All analysis are performed on a large real-world dataset recording all purchases from millions of customers across the west coast of Italy.},
	doi = {10.1140/epjds/s13688-015-0051-3},
	url = {http://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0051-3},
	author = {Michele Coscia and Diego Pennacchioli and Fosca Giannotti}
}
@conference {623,
	title = {The patterns of musical influence on the Last.Fm social network},
	booktitle = {22nd Italian Symposium on Advanced Database Systems, {SEBD} 2014, Sorrento Coast, Italy, June 16-18, 2014.},
	year = {2014},
	author = {Diego Pennacchioli and Giulio Rossetti and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti and Michele Coscia}
}
@conference {565,
	title = {A Privacy Risk Model for Trajectory Data},
	booktitle = {Trust Management {VIII} - 8th {IFIP} {WG} 11.11 International Conference, {IFIPTM} 2014, Singapore, July 7-10, 2014. Proceedings},
	year = {2014},
	pages = {125{\textendash}140},
	abstract = {Time sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data.},
	doi = {10.1007/978-3-662-43813-8_9},
	url = {http://dx.doi.org/10.1007/978-3-662-43813-8_9},
	author = {Anirban Basu and Anna Monreale and Juan Camilo Corena and Fosca Giannotti and Dino Pedreschi and Shinsaku Kiyomoto and Yutaka Miyake and Tadashi Yanagihara and Roberto Trasarti}
}
@article {EPJ14,
	title = {Privacy-by-Design in Big Data Analytics and Social Mining},
	journal = {EPJ Data Science},
	volume = {10},
	year = {2014},
	note = {2014:10},
	abstract = {Privacy is ever-growing concern in our society and is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving human personal sensitive information. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. As a result, privacy preservation simply cannot be accomplished by de-identification alone. In this paper, we propose the privacy-by-design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start.},
	doi = {10.1140/epjds/s13688-014-0010-4},
	author = {Anna Monreale and S Rinzivillo and Francesca Pratesi and Fosca Giannotti and Dino Pedreschi}
}
@conference {976,
	title = {Process mining event logs from FLOSS data: state of the art and perspectives},
	booktitle = {International Conference on Software Engineering and Formal Methods},
	year = {2014},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Free/Libre Open Source Software (FLOSS) is a phenomenon that has undoubtedly triggered extensive research endeavors. At the heart of these initiatives is the ability to mine data from FLOSS repositories with the hope of revealing empirical evidence to answer existing questions on the FLOSS development process. In spite of the success produced with existing mining techniques, emerging questions about FLOSS data require alternative and more appropriate ways to explore and analyse such data.

In this paper, we explore a different perspective called process mining. Process mining has been proved to be successful in terms of tracing and reconstructing process models from data logs (event logs). The chief objective of our analysis is threefold. We aim to achieve: (1) conformance to predefined models; (2) discovery of new model patterns; and, finally, (3) extension to predefined models.

},
	doi = {10.1007/978-3-319-15201-1_12},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {725,
	title = {The purpose of motion: Learning activities from Individual Mobility Networks},
	booktitle = {International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014},
	year = {2014},
	doi = {10.1109/DSAA.2014.7058090},
	url = {http://dx.doi.org/10.1109/DSAA.2014.7058090},
	author = {S Rinzivillo and Lorenzo Gabrielli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti}
}
@conference {535,
	title = {Pisa Tourism fluxes Observatory: deriving mobility indicators from GSM call habits},
	booktitle = {NetMob Conference 2013},
	year = {2013},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@conference {615,
	title = {Privacy-Aware Distributed Mobility Data Analytics},
	booktitle = {SEBD},
	year = {2013},
	address = {Roccella Jonica},
	abstract = {We propose an approach to preserve privacy in an analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because they may describe typical movement behaviors and therefore be used for re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.
},
	author = {Francesca Pratesi and Anna Monreale and Hui Wendy Wang and S Rinzivillo and Dino Pedreschi and Gennady Andrienko and Natalia Andrienko}
}
@inbook {571,
	title = {Privacy-Preserving Distributed Movement Data Aggregation},
	booktitle = {Geographic Information Science at the Heart of Europe},
	series = {Lecture Notes in Geoinformation and Cartography},
	year = {2013},
	pages = {225-245},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {We propose a novel approach to privacy-preserving analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because people{\textquoteright}s whereabouts have the potential to reveal intimate personal traits, such as religious or sexual preferences, and may allow re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.},
	isbn = {978-3-319-00614-7},
	doi = {10.1007/978-3-319-00615-4_13},
	url = {http://dx.doi.org/10.1007/978-3-319-00615-4_13},
	author = {Anna Monreale and Hui Wendy Wang and Francesca Pratesi and S Rinzivillo and Dino Pedreschi and Gennady Andrienko and Natalia Andrienko},
	editor = {Vandenbroucke, Danny and Bucher, B{\'e}n{\'e}dicte and Crompvoets, Joep}
}
@article {478,
	title = {Privacy-Preserving Mining of Association Rules From Outsourced Transaction Databases},
	journal = { IEEE Systems Journal},
	year = {2013},
	abstract = {Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the corporation (data owner). To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose an attack model based on background knowledge and devise a scheme for privacy preserving outsourced mining. Our scheme ensures that each transformed item is indistinguishable with respect to the attacker{\textquoteright}s background knowledge, from at least k-1 other transformed items. Our comprehensive experiments on a very large and real transaction database demonstrate that our techniques are effective, scalable, and protect privacy.},
	doi = {10.1109/JSYST.2012.2221854},
	author = {Fosca Giannotti and L.V.S. Lakshmanan and Anna Monreale and Dino Pedreschi and Hui Wendy Wang}
}
@conference {533,
	title = {A Proactive Ap- plication to Monitor Truck Fleets},
	booktitle = {Mobile Data Management Conference, 2013},
	year = {2013},
	author = {Fabio Da Costa Albuquerque and Marco A. Casanova and Marcelo Tilio M. de Carvalho and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Chiara Renso}
}
@conference {481,
	title = {Privacy-preserving data mining from outsourced databases.},
	booktitle = { the 3rd International Conference on Computers, Privacy, and Data Protection: An element of choice },
	year = {2011},
	month = {2011},
	abstract = {Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service: a company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the outsourced database and the knowledge extract from it by data mining are considered private property of the data owner. To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing a data mining task within a corporate privacy-preserving framework. We propose a scheme for privacy-preserving outsourced mining which offers a formal protection against information disclosure, and show that the data owner can recover the correct data mining results efficiently.},
	doi = {10.1007/978-94-007-0641-5_19},
	author = {Fosca Giannotti and L.V.S. Lakshmanan and Anna Monreale and Dino Pedreschi and Hui Wendy Wang}
}
@article {jocs2011,
	title = {The pursuit of hubbiness: Analysis of hubs in large multidimensional networks},
	journal = {J. Comput. Science},
	volume = {2},
	number = {3},
	year = {2011},
	pages = {223-237},
	abstract = {Hubs are highly connected nodes within a network. In complex network analysis, hubs have been widely studied, and are at the basis of many tasks, such as web search and epidemic outbreak detection. In reality, networks are often multidimensional, i.e., there can exist multiple connections between any pair of nodes. In this setting, the concept of hub depends on the multiple dimensions of the network, whose interplay becomes crucial for the connectedness of a node. In this paper, we characterize multidimensional hubs. We consider the multidimensional generalization of the degree and introduce a new class of measures, that we call Dimension Relevance, aimed at analyzing the importance of different dimensions for the hubbiness of a node. We assess the meaningfulness of our measures by comparing them on real networks and null models, then we study the interplay among dimensions and their effect on node connectivity. Our findings show that: (i) multidimensional hubs do exist and their characterization yields interesting insights and (ii) it is possible to detect the most influential dimensions that cause the different hub behaviors. We demonstrate the usefulness of multidimensional analysis in three real world domains: detection of ambiguous query terms in a word{\textendash}word query log network, outlier detection in a social network, and temporal analysis of behaviors in a co-authorship network.},
	doi = {10.1016/j.jocs.2011.05.009},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@conference {MonrealeTRPB10,
	title = {Preserving privacy in semantic-rich trajectories of human mobility},
	booktitle = {SPRINGL},
	year = {2010},
	pages = {47-54},
	abstract = {The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for analyzing and mining human mobility, but new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses even greater privacy threats w.r.t. raw geometric location data. In this paper we propose a privacy model defining the attack model of semantic trajectory linking, together with a privacy notion, called c-safety. This method provides an upper bound to the probability of inferring that a given person, observed in a sequence of nonsensitive places, has also stopped in any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on a real-life GPS trajectory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.},
	doi = {10.1145/1868470.1868481},
	author = {Anna Monreale and Roberto Trasarti and Chiara Renso and Dino Pedreschi and Vania Bogorny}
}
@conference {1279,
	title = {Poverty as a Social Condition: a Case Study on a Small Municipality in Tuscany},
	booktitle = {Global Recession: Regional Impacts on Housing, Jobs, Health and Wellbeing},
	year = {2009},
	publisher = {SEAFORD},
	organization = {SEAFORD},
	author = {Tomei, Gabriele and Michela Natilli}
}
@conference {DBLP:conf/esoric,
	title = {Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining},
	booktitle = {PiLBA},
	year = {2008},
	abstract = {Sequential pattern mining is a major research field in knowledge
discovery and data mining. Thanks to the increasing availability of
transaction data, it is now possible to provide new and improved services
based on users{\textquoteright} and customers{\textquoteright} behavior. However, this puts the citizen{\textquoteright}s
privacy at risk. Thus, it is important to develop new privacy-preserving
data mining techniques that do not alter the analysis results significantly.
In this paper we propose a new approach for anonymizing sequential
data by hiding infrequent, and thus potentially sensible, subsequences.
Our approach guarantees that the disclosed data are k-anonymous and
preserve the quality of extracted patterns. An application to a real-world
moving object database is presented, which shows the effectiveness of our
approach also in complex contexts.},
	url = {https://air.unimi.it/retrieve/handle/2434/52786/106397/ProceedingsPiLBA08.pdf$\#$page=44},
	author = {Ruggero G. Pensa and Anna Monreale and Fabio Pinelli and Dino Pedreschi}
}
@inbook {PedreschiBTVAMMS,
	title = {Privacy Protection: Regulations and Technologies, Opportunities and Threats},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {101-119},
	author = {Dino Pedreschi and Francesco Bonchi and Franco Turini and Vassilios S. Verykios and Maurizio Atzori and Bradley Malin and Bart Moelans and Y{\"u}cel Saygin}
}
@conference {DBLP:conf/mdm/AtzoriBGPA07,
	title = {Privacy-Aware Knowledge Discovery from Location Data},
	booktitle = {MDM},
	year = {2007},
	pages = {283-287},
	author = {Maurizio Atzori and Francesco Bonchi and Fosca Giannotti and Dino Pedreschi and Osman Abul}
}
@conference {467,
	title = {PUSHING CONSTRAINTS IN ASSOCIATION RULE MINING: AN ONTOLOGY-BASED APPROACH },
	booktitle = { IADIS International Conference WWW/Internet 2007},
	year = {2007},
	month = {2007},
	isbn = {978-972-8924-44-7},
	url = {http://www.iadisportal.org/digital-library/mdownload/pushing-constraints-in-association-rule-mining-an-ontology-based-approach},
	author = {Barbara Furletti and Andrea Bellandi and Andrea Romei and Valerio Grossi}
}
@conference {DBLP:conf/dagstuhl/BonchiG04,
	title = {Pushing Constraints to Detect Local Patterns},
	booktitle = {Local Pattern Detection},
	year = {2004},
	pages = {1-19},
	author = {Francesco Bonchi and Fosca Giannotti}
}
@conference {1281,
	title = {Personal income in the gross and net forms: applications of the Siena Micro-Simulation Model (SM2)},
	booktitle = {conference of the Societ{\`a} Italiana di Economia, Demografia e Statistica (SIEDS), Campobasso},
	year = {2003},
	author = {Verma, V and Betti, G and Ballini, F and Michela Natilli and Galgani, S}
}
@conference {DBLP:conf/sebd/BonchiGMP03,
	title = {Pre-processing for Constrained Pattern Mining},
	booktitle = {SEBD},
	year = {2003},
	pages = {519-530},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@article {DBLP:journals/amai/GiannottiGSZ97,
	title = {Programming with Non-Determinism in Deductive Databases},
	journal = {Ann. Math. Artif. Intell.},
	volume = {19},
	number = {1-2},
	year = {1997},
	pages = {97-125},
	author = {Fosca Giannotti and Sergio Greco and Domenico Sacc{\`a} and Carlo Zaniolo}
}
@conference {DBLP:conf/iclp/Pedreschi94,
	title = {A Proof Method for Runtime Properties of Prolog Programs},
	booktitle = {ICLP},
	year = {1994},
	pages = {584-598},
	author = {Dino Pedreschi}
}
@conference {DBLP:conf/agp/MascellaniP94,
	title = {Proving termination of Prolog programs},
	booktitle = {GULP-PRODE (1)},
	year = {1994},
	pages = {46-61},
	author = {Paolo Mascellani and Dino Pedreschi}
}
@conference {DBLP:conf/tacs/AptP91,
	title = {Proving Termination of General Prolog Programs},
	booktitle = {TACS},
	year = {1991},
	pages = {265-289},
	author = {Krzysztof R. Apt and Dino Pedreschi}
}
@conference {DBLP:conf/fgcs/BertolinoMMNPT88,
	title = {A Progress Report on the LML Project},
	booktitle = {FGCS},
	year = {1988},
	pages = {675-684},
	author = {Bruno Bertolino and Paolo Mancarella and Luigi Meo and Luca Nini and Dino Pedreschi and Franco Turini}
}
