@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}
}
@article {1556,
	title = {Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions},
	journal = {Applied Network Science},
	volume = {8},
	number = {1},
	year = {2023},
	pages = {1{\textendash}19},
	doi = {https://doi.org/10.1002/widm.1356},
	author = {Failla, Andrea and Citraro, Salvatore and Rossetti, Giulio}
}
@conference {1517,
	title = {AUC-based Selective Classification},
	booktitle = {International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain},
	year = {2023},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The approach is specifically targeted at optimizing the AUC. We provide both theoretical justifications and a novel algorithm, called AUCROSS, to achieve such a goal. Experiments show that our method succeeds in trading-off coverage for AUC, improving over existing selective classification methods targeted at optimizing accuracy.},
	url = {https://proceedings.mlr.press/v206/pugnana23a.html},
	author = {Andrea Pugnana and Salvatore Ruggieri}
}
@conference {1558,
	title = {Attribute-aware Community Events in Feature-rich Dynamic Networks},
	booktitle = {Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2022{\textemdash}Book of Abstracts},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	author = {Failla, Andrea and Mazzoni, Federico and Citraro, Salvatore}
}
@conference {1557,
	title = {Attributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	doi = {https://doi.org/10.1007/978-3-031-21127-0_13},
	author = {Failla, Andrea and Citraro, Salvatore and Rossetti, Giulio}
}
@conference {1408,
	title = {Analysis and Visualization of Performance Indicators in University Admission Tests},
	booktitle = {Formal Methods. FM 2019 International Workshops},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the question or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the composition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.},
	isbn = {978-3-030-54994-7},
	doi = {https://doi.org/10.1007/978-3-030-54994-7_14},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-54994-7_14},
	author = {Michela Natilli and Daniele Fadda and S Rinzivillo and Dino Pedreschi and Licari, Federica},
	editor = {Sekerinski, Emil and Moreira, Nelma and Oliveira, Jos{\'e} N. and Ratiu, Daniel and Riccardo Guidotti and Farrell, Marie and Luckcuck, Matt and Marmsoler, Diego and Campos, Jos{\'e} and Astarte, Troy and Gonnord, Laure and Cerone, Antonio and Couto, Luis and Dongol, Brijesh and Kutrib, Martin and Monteiro, Pedro and Delmas, David}
}
@article {1370,
	title = {ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks},
	journal = {Applied Network Science},
	volume = {5},
	number = {1},
	year = {2020},
	pages = {1{\textendash}23},
	abstract = {Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.},
	doi = {https://doi.org/10.1007/s41109-020-00270-6},
	url = {https://link.springer.com/article/10.1007/s41109-020-00270-6},
	author = {Giulio Rossetti}
}
@article {1336,
	title = {Artificial Intelligence (AI): new developments and innovations applied to e-commerce},
	year = {2020},
	month = {05/2020},
	institution = {European Parliament{\textquoteright}s committee on the Internal Market and Consumer Protection},
	abstract = {This in-depth analysis discusses the opportunities and challenges brought by the recent and the foreseeable developments of Artificial Intelligence into online platforms and marketplaces. The paper advocates the importance to support tustworthy, explainable AI (in order to fight discrimination and manipulation, and empower citizens), and societal-aware AI (in order to fight polarisation, monopolistic concentration and excessive inequality, and pursue diversity and openness).
This document was provided by the Policy Department for Economic, Scientific and Quality of Life Policies at the request of the committee on the Internal Market and Consumer Protection (IMCO).},
	url = {https://www.europarl.europa.eu/thinktank/en/document.html?reference=IPOL_IDA(2020)648791},
	author = {Dino Pedreschi and Ioanna Miliou}
}
@article {1344,
	title = {Authenticated Outlier Mining for Outsourced Databases},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	volume = {17},
	year = {2020},
	month = {Jan-03-2020},
	pages = {222 - 235},
	abstract = {The Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records (ARs) into the dataset, from which it constructs a set of artificial outliers (AOs) and artificial non-outliers (ANOs). The AOs and ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify ANOs and AOs without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.},
	issn = {1545-5971},
	doi = {10.1109/TDSC.885810.1109/TDSC.2017.2754493},
	url = {https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8858https://ieeexplore.ieee.org/document/8048342/http://xplorestaging.ieee.org/ielx7/8858/9034462/08048342.pdf?arnumber=8048342https://ieeexplore.ieee.org/ielam/8858/9034462/8048342-aam.pdf},
	author = {Dong, Boxiang and Wang, Hui and Anna Monreale and Dino Pedreschi and Fosca Giannotti and Guo, Wenge}
}
@article {1260,
	title = {The AI black box Explanation Problem},
	journal = {ERCIM NEWS},
	number = {116},
	year = {2019},
	pages = {12{\textendash}13},
	author = {Riccardo Guidotti and Anna Monreale and Dino Pedreschi}
}
@article {1216,
	title = {Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model},
	journal = {PloS one},
	volume = {14},
	number = {3},
	year = {2019},
	pages = {e0213246},
	abstract = {The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population.},
	doi = {10.1371/journal.pone.0213246},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213246},
	author = {Alina Sirbu and Dino Pedreschi and Fosca Giannotti and Kert{\'e}sz, J{\'a}nos}
}
@article {1245,
	title = {Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations},
	journal = {Sensors},
	volume = {19},
	number = {14},
	year = {2019},
	pages = {3163},
	abstract = {Wearable physiological monitors have become increasingly popular, often worn during people{\textquoteright}s daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.},
	doi = {10.3390/s19143163},
	url = {https://www.mdpi.com/1424-8220/19/14/3163},
	author = {Morelli, Davide and Alessio Rossi and Cairo, Massimo and Clifton, David A}
}
@article {1179,
	title = {Active and passive diffusion processes in complex networks},
	journal = {Applied network science},
	volume = {3},
	number = {1},
	year = {2018},
	pages = {42},
	abstract = {Ideas, information, viruses: all of them, with their mechanisms, spread over the complex social information, viruses: all tissues described by our interpersonal relations. Usually, to simulate and understand the unfolding of such complex phenomena are used general mathematical models; these models act agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such degree of abstraction makes it easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, incorrect, simulation outcomes. In this work we introduce the concepts of active and passive diffusion to discriminate the degree in which individuals choice affect the overall spreading of content over a social graph. Moving from the analysis of a well-known passive diffusion schema, the Threshold model (that can be used to model peer-pressure related processes), we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation.

Our analysis, performed both in synthetic and real-world data, underline that the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches to capture the real complexity of the simulated system better.},
	doi = {https://doi.org/10.1007/s41109-018-0100-5},
	url = {https://link.springer.com/article/10.1007/s41109-018-0100-5},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@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}
}
@article {1135,
	title = {Assessing the Stability of Interpretable Models},
	journal = {arXiv preprint arXiv:1810.09352},
	year = {2018},
	abstract = {Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.},
	author = {Riccardo Guidotti and Salvatore Ruggieri}
}
@inbook {848,
	title = {Applications for Environmental Sensing in EveryAware},
	booktitle = {Participatory Sensing, Opinions and Collective Awareness},
	year = {2017},
	pages = {135{\textendash}155},
	publisher = {Springer},
	organization = {Springer},
	abstract = {This chapter provides a technical description of the EveryAware applications for air quality and noise monitoring. Specifically, we introduce AirProbe, for measuring air quality, and WideNoise Plus for estimating environmental noise. We also include an overview on hardware components and smartphone-based measurement technology, and we present the according web backend, e.g., providing for real-time tracking, data storage, analysis and visualizations.

},
	doi = {10.1007/978-3-319-25658-0_7},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-25658-0_7},
	author = {Atzmueller, Martin and Becker, Martin and Molino, Andrea and Mueller, Juergen and Peters, Jan and Alina Sirbu}
}
@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 {1178,
	title = {Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models},
	journal = {Royal Society open science},
	volume = {4},
	number = {5},
	year = {2017},
	pages = {160950},
	abstract = {The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.},
	author = {Cecilia Panigutti and Tizzoni, Michele and Bajardi, Paolo and Smoreda, Zbigniew and Colizza, Vittoria}
}
@article {1051,
	title = {Authenticated Outlier Mining for Outsourced Databases},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	year = {2017},
	abstract = {The Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records (ARs) into the dataset, from which it constructs a set of artificial outliers (AOs) and artificial non-outliers (ANOs). The AOs and ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify ANOs and AOs without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.},
	doi = {10.1109/TDSC.2017.2754493},
	url = {https://ieeexplore.ieee.org/document/8048342/},
	author = {Dong, Boxiang and Hui Wendy Wang and Anna Monreale and Dino Pedreschi and Fosca Giannotti and W Guo}
}
@booklet {962,
	title = {Advances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings},
	year = {2016},
	abstract = {This book constitutes the refereed proceedings of the 12th International Conference and  School of Network Science, NetSci-X 2016, held in Wroclaw, Poland, in January 2016. The 12 full and 6 short papers were carefully reviewed and selected from 59 submissions. The papers deal with the study of network models in domains ranging from biology and physics to computer science, from financial markets to cultural integration, and from social media to infectious diseases.},
	doi = {10.1007/978-3-319-28361-6},
	author = {Wierzbicki, Adam and Brandes, Ulrik and Schweitzer, Frank and Dino Pedreschi}
}
@article {961,
	title = {An analytical framework to nowcast well-being using mobile phone data},
	journal = {International Journal of Data Science and Analytics},
	volume = {2},
	number = {1-2},
	year = {2016},
	pages = {75{\textendash}92},
	abstract = {An intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users{\textquoteright} trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly {\textquotedblleft}nowcast{\textquotedblright} the well-being and the socio-economic development of a territory.},
	doi = {10.1007/s41060-016-0013-2},
	author = {Luca Pappalardo and Maarten Vanhoof and Lorenzo Gabrielli and Zbigniew Smoreda and Dino Pedreschi and Fosca Giannotti}
}
@proceedings {874,
	title = {{\textquotedblleft}Are we playing like Music-Stars?{\textquotedblright} Placing Emerging Artists on the Italian Music Scene},
	year = {2016},
	month = {09/2016},
	address = {Riva del Garda},
	abstract = {The Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a deep study of the Italian music scene and how the new generation ofmusicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional mu- sic and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results ob- tained are a first attempt to outline some rules suggesting how to reach the success in the musical Italian scene.},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti}
}
@conference {882,
	title = {Audio Ergo Sum},
	booktitle = {Federation of International Conferences on Software Technologies: Applications and Foundations},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Nobody can state {\textquotedblleft}Rock is my favorite genre{\textquotedblright} or {\textquotedblleft}David Bowie is my favorite artist{\textquotedblright}. We defined a Personal Listening Data Model able to capture musical preferences through indicators and patterns, and we discovered that we are all characterized by a limited set of musical preferences, but not by a unique predilection. The empowered capacity of mobile devices and their growing adoption in our everyday life is generating an enormous increment in the production of personal data such as calls, positioning, online purchases and even music listening. Musical listening is a type of data that has started receiving more attention from the scientific community as consequence of the increasing availability of rich and punctual online data sources. Starting from the listening of 30k Last.Fm users, we show how the employment of the Personal Listening Data Models can provide higher levels of self-awareness. In addition, the proposed model will enable the development of a wide range of analysis and musical services both at personal and at collective level.},
	doi = {10.1007/978-3-319-50230-4_5},
	author = {Riccardo Guidotti and Giulio Rossetti and Dino Pedreschi}
}
@conference {978,
	title = {An abstract state machine (ASM) representation of learning process in FLOSS communities},
	booktitle = {International Conference on Software Engineering and Formal Methods},
	year = {2014},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Free/Libre Open Source Software (FLOSS) communities as collaborative environments enable the occurrence of learning between participants in these groups. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes. One successful way of doing this is through specification methods. In this paper, we describe the adoption of Abstract States Machines (ASMs) as a specification methodology for the description of learning processes in FLOSS. The goal of this endeavor is to represent the many possible steps and/or activities FLOSS participants go through during interactions that can be categorized as learning processes. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions.},
	doi = {10.1007/978-3-319-15201-1_15},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@article {564,
	title = {Anonymity preserving sequential pattern mining},
	journal = {Artif. Intell. Law},
	volume = {22},
	number = {2},
	year = {2014},
	pages = {141{\textendash}173},
	abstract = {The increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility.},
	doi = {10.1007/s10506-014-9154-6},
	url = {http://dx.doi.org/10.1007/s10506-014-9154-6},
	author = {Anna Monreale and Dino Pedreschi and Ruggero G. Pensa and Fabio Pinelli}
}
@conference {983,
	title = {Anti-discrimination analysis using privacy attack strategies},
	booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	year = {2014},
	publisher = {Springer, Berlin, Heidelberg},
	organization = {Springer, Berlin, Heidelberg},
	abstract = {Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Fr{\`e}chet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.},
	doi = {10.1007/978-3-662-44851-9_44},
	author = {Salvatore Ruggieri and Sara Hajian and Kamiran, Faisal and Zhang, Xiangliang}
}
@proceedings {528,
	title = {Analysis of GSM Calls Data for Understanding User Mobility Behavior},
	year = {2013},
	address = {Santa Clara, California},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@article {542,
	title = {Assessing the Attractiveness of Places with Movement Data },
	journal = {Journal of Information and Data Management},
	volume = {4},
	number = {2},
	year = {2013},
	month = {2013},
	author = {Andr{\'e} Salvaro Furtado and Renato Fileto and Chiara Renso}
}
@conference {536,
	title = {Average Speed Estimation For Road Networks Based On GPS Raw Trajectories},
	booktitle = {ICEIS Conference},
	year = {2013},
	author = {Ivanildo Barbosa and Marco A. Casanova and Chiara Renso and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do}
}
@article {795,
	title = {Awareness and learning in participatory noise sensing.},
	journal = {PLoS One},
	volume = {8},
	year = {2013},
	month = {2013},
	pages = {e81638},
	abstract = {<p>The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.</p>},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0081638},
	author = {Becker, Martin and Saverio Caminiti and Fiorella, Donato and Francis, Louise and Pietro Gravino and Haklay, Mordechai Muki and Hotho, Andreas and Vittorio Loreto and Mueller, Juergen and Ricchiuti, Ferdinando and Vito D P Servedio and Alina Sirbu and Francesca Tria}
}
@conference {687,
	title = {An Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants},
	booktitle = {Proceedings of the 3rd International Conference on Ambient Systems, Networks and Technologies {(ANT} 2012), the 9th International Conference on Mobile Web Information Systems (MobiWIS-2012), Niagara Falls, Ontario, Canada, August 27-29, 2012},
	year = {2012},
	doi = {10.1016/j.procs.2012.08.001},
	url = {http://dx.doi.org/10.1016/j.procs.2012.08.001},
	author = {Tom Bellemans and Sebastian Bothe and Sungjin Cho and Fosca Giannotti and Davy Janssens and Luk Knapen and Christine K{\"o}rner and Michael May and Mirco Nanni and Dino Pedreschi and Hendrik Stange and Roberto Trasarti and Ansar-Ul-Haque Yasar and Geert Wets}
}
@article {488,
	title = {Analisi di Mobilita{\textquoteright} con dati eterogenei},
	year = {2012},
	institution = {ISTI - CNR},
	address = {Pisa},
	author = {Barbara Furletti and Roberto Trasarti and Lorenzo Gabrielli and S Rinzivillo and Luca Pappalardo and Fosca Giannotti}
}
@conference {480,
	title = {Anonymity: a Comparison between the Legal and Computer Science Perspectives.},
	booktitle = {The 5rd International Conference on Computers, Privacy, and Data Protection: {\textquotedblleft}European Data Protection: Coming of Age{\textquotedblright}},
	year = {2012},
	month = {2012},
	abstract = {Privacy preservation has emerged as a major challenge in ICT. One possible solution for enforcing privacy is to guarantee anonymity. Indeed, according to international regulations, no restriction is applied to the handling of anonymous data. Consequently, in the past years the notion of anonymity has been extensively studied by two different communities: Law researchers and professionals that propose definitions of privacy regulations, and Computer Scientists attempting to provide technical solutions for enforcing the legal requirements.

In this contribution we address the problem with an interdisciplinary approach, in the aim to encourage the reciprocal understanding and collaboration between researchers in the two areas. To achieve this, we compare the different notions of anonymity provided in the European data protection Law with the formal models proposed in Computer Science. This analysis allows us to identify the main similarities and differences between the two points of view, hence highlighting the need for a joint research effort.},
	doi = {10.1007/978-94-007-5170-5_4},
	author = {S Mascetti and Anna Monreale and A Ricci and A. Gerino}
}
@conference {479,
	title = {AUDIO: An Integrity Auditing Framework of Outlier-Mining-as-a-Service Systems.},
	booktitle = {Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2012 },
	year = {2012},
	month = {2012},
	abstract = {Spurred by developments such as cloud computing, there has been considerable recent interest in the data-mining-as-a-service paradigm. Users lacking in expertise or computational resources can outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises issues about result integrity: how can the data owner verify that the mining results returned by the server are correct? In this paper, we present AUDIO, an integrity auditing framework for the specific task of distance-based outlier mining outsourcing. It provides efficient and practical verification approaches to check both completeness and correctness of the mining results. The key idea of our approach is to insert a small amount of artificial tuples into the outsourced data; the artificial tuples will produce artificial outliers and non-outliers that do not exist in the original dataset. The server{\textquoteright}s answer is verified by analyzing the presence of artificial outliers/non-outliers, obtaining a probabilistic guarantee of correctness and completeness of the mining result. Our empirical results show the effectiveness and efficiency of our method.},
	doi = {10.1007/978-3-642-33486-3_1},
	author = {R.Liu and Hui Wendy Wang and Anna Monreale and Dino Pedreschi and Fosca Giannotti and W Guo}
}
@conference {NanniTRGP10,
	title = {Advanced knowledge discovery on movement data with the GeoPKDD system},
	booktitle = {EDBT},
	year = {2010},
	pages = {693-696},
	author = {Mirco Nanni and Roberto Trasarti and Chiara Renso and Fosca Giannotti and Dino Pedreschi}
}
@conference {NanniTRGP10,
	title = {Advanced knowledge discovery on movement data with the GeoPKDD system},
	booktitle = {EDBT},
	year = {2010},
	pages = {693-696},
	author = {Mirco Nanni and Roberto Trasarti and Chiara Renso and Fosca Giannotti and Dino Pedreschi}
}
@conference {pakdd2010,
	title = {As Time Goes by: Discovering Eras in Evolving Social Networks},
	booktitle = {PAKDD (1)},
	year = {2010},
	pages = {81-90},
	abstract = {Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus instead on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset; we illustrate how the discovered temporal clustering highlights the crucial moments when the network had profound changes in its structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.},
	doi = {10.1007/978-3-642-13657-3_11},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@proceedings {242,
	title = {Anonymous Sequences from Trajectory Data},
	year = {2009},
	edition = {17},
	address = {Camogli, Italy},
	author = {Ruggero G. Pensa and Anna Monreale and Fabio Pinelli and Dino Pedreschi}
}
@article {DBLP:journals/vldb/AtzoriBGP08,
	title = {Anonymity preserving pattern discovery},
	journal = {VLDB J.},
	volume = {17},
	number = {4},
	year = {2008},
	pages = {703-727},
	author = {Maurizio Atzori and Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@article {DBLP:journals/geoinformatica/RaffaetaCCGMPRST08,
	title = {An Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology},
	journal = {GeoInformatica},
	volume = {12},
	number = {1},
	year = {2008},
	pages = {37-72},
	author = {Alessandra Raffaet{\`a} and T. Ceccarelli and D. Centeno and Fosca Giannotti and A. Massolo and Christine Parent and Chiara Renso and Stefano Spaccapietra and Franco Turini}
}
@article {geoinfo,
	title = {An Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology},
	year = {2008},
	note = {Geoinformatica, Volume 12, Number 1 / March,},
	author = {T. Ceccarelli and D. Centeno and Fosca Giannotti and A. Massolo and Christine Parent and Alessandra Raffaet{\`a} and Chiara Renso and Stefano Spaccapietra and Franco Turini}
}
@article {DBLP:journals/csse/AtzoriBGP05,
	title = {Anonymity and data mining},
	journal = {Comput. Syst. Sci. Eng.},
	volume = {20},
	number = {5},
	year = {2005},
	author = {Maurizio Atzori and Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/pkdd/BonchiGMP03,
	title = {Adaptive Constraint Pushing in Frequent Pattern Mining},
	booktitle = {PKDD},
	year = {2003},
	pages = {47-58},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@article {AART97,
	title = {Applying Restriction Constraint to Deductive Databases},
	journal = {Annals of Mathematics and Artificial Intelligence},
	year = {1997},
	note = {1997},
	pages = {3{\textendash}25},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/amai/AquilinoART97,
	title = {Applying Restriction Constraints to Deductive Databases},
	journal = {Ann. Math. Artif. Intell.},
	volume = {19},
	number = {1-2},
	year = {1997},
	pages = {3-25},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/forte/FioreG94,
	title = {An abstract interpreter for the specification language LOTOS},
	booktitle = {FORTE},
	year = {1994},
	pages = {309-323},
	author = {Franco Fiore and Fosca Giannotti}
}
@conference {DBLP:conf/agp/BrogiRT94,
	title = {Amalgamating Language and Meta-language for Composing Logic Programs},
	booktitle = {GULP-PRODE (2)},
	year = {1994},
	pages = {408-422},
	author = {Antonio Brogi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/sas/AmatoGM92,
	title = {Analysis of Concurrent Transactions in a Functional Database Programming Language},
	booktitle = {WSA},
	year = {1992},
	pages = {174-184},
	author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto}
}
@conference {DBLP:conf/slp/MancarellaPRT90,
	title = {Algebraic Properties of a Class of Logic Programs},
	booktitle = {NACLP},
	year = {1990},
	pages = {23-39},
	author = {Paolo Mancarella and Dino Pedreschi and Marina Rondinelli and Marco Tagliatti}
}
@conference {DBLP:conf/iclp/MancarellaP88,
	title = {An Algebra of Logic Programs},
	booktitle = {ICLP/SLP},
	year = {1988},
	pages = {1006-1023},
	author = {Paolo Mancarella and Dino Pedreschi}
}
