2019
Journal article
Open Access
The AI black box explanation problem
Guidotti R., Monreale A., Pedreschi D.
Explainable AI is an essential component of a "Human AI", i.e., an AI that expands human experience, instead of replacing it. It will be impossible to gain the trust of people in AI tools that make crucial decisions in an opaque way without explaining the rationale followed, especially in areas where we do not want to completely delegate decisions to machines.Source: ERCIM news (2019): 12–13.
Project(s): SoBigData
Guidotti R., Monreale A., Pedreschi D.
Explainable AI is an essential component of a "Human AI", i.e., an AI that expands human experience, instead of replacing it. It will be impossible to gain the trust of people in AI tools that make crucial decisions in an opaque way without explaining the rationale followed, especially in areas where we do not want to completely delegate decisions to machines.Source: ERCIM news (2019): 12–13.
Project(s): SoBigData
See at:
ercim-news.ercim.eu 
| ISTI Repository | CNR ExploRA
2010
Contribution to book
Restricted
Anonymity technologies for privacy-preserving data publishing and mining
Monreale A., Pedreschi D., Pensa R. G.
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. Data mining is the key step in the process of Knowledge Discovery in Databases, the so-called KDD pro- cess. The knowledge discovered in data by means of sophisticated data mining techniques is leading to a new generation of personalized intelligent services. The dark side of this story is that the very same collection technologies gather personal, often sensitive, data, so that the opportunities of discovering knowl- edge increase hand in hand with the risks of privacy violation.Source: Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques, edited by Francesco Bonchi, Elena Ferrari, pp. 3–33. Boca Raton: CRC Press, 2010
Monreale A., Pedreschi D., Pensa R. G.
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. Data mining is the key step in the process of Knowledge Discovery in Databases, the so-called KDD pro- cess. The knowledge discovered in data by means of sophisticated data mining techniques is leading to a new generation of personalized intelligent services. The dark side of this story is that the very same collection technologies gather personal, often sensitive, data, so that the opportunities of discovering knowl- edge increase hand in hand with the risks of privacy violation.Source: Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques, edited by Francesco Bonchi, Elena Ferrari, pp. 3–33. Boca Raton: CRC Press, 2010
See at:
www.crcpress.com 
| CNR ExploRA
2013
Conference article
Unknown
On multidimensional network measures
Magnani M., Monreale A., Rossetti G., Giannotti F.
Networks, i.e., sets of interconnected entities, are ubiquitous, spanning disciplines as diverse as sociology, biology and computer sci- ence. The recent availability of large amounts of network data has thus provided a unique opportunity to develop models and analysis tools ap- plicable to a wide range of scenarios. However, real-world phenomena are often more complex than existing graph data models. One relevant ex- ample concerns the numerous types of social relationships (or edges) that can be present between individuals in a social network. In this short pa- per we present a uni ed model and a set of measures recently developed to represent and analyze network data with multiple types of edges.Source: SEDB 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 215–222, Roccella Jonica, Reggio Calabria, Italy, 30 June - 3 July 2013
Magnani M., Monreale A., Rossetti G., Giannotti F.
Networks, i.e., sets of interconnected entities, are ubiquitous, spanning disciplines as diverse as sociology, biology and computer sci- ence. The recent availability of large amounts of network data has thus provided a unique opportunity to develop models and analysis tools ap- plicable to a wide range of scenarios. However, real-world phenomena are often more complex than existing graph data models. One relevant ex- ample concerns the numerous types of social relationships (or edges) that can be present between individuals in a social network. In this short pa- per we present a uni ed model and a set of measures recently developed to represent and analyze network data with multiple types of edges.Source: SEDB 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 215–222, Roccella Jonica, Reggio Calabria, Italy, 30 June - 3 July 2013
See at: CNR ExploRA
2015
Contribution to book
Open Access
Retrieving points of interest from human systematic movements
Guidotti R., Monreale A., Rinzivillo S., Pedreschi D., Giannotti F.
Human mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers' systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions.Source: Software Engineering and Formal Methods, edited by Carlos Canal, Akram Idani, pp. 294–308, 2015
DOI: 10.1007/978-3-319-15201-1_19
Project(s): PETRA
Metrics:
Guidotti R., Monreale A., Rinzivillo S., Pedreschi D., Giannotti F.
Human mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers' systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions.Source: Software Engineering and Formal Methods, edited by Carlos Canal, Akram Idani, pp. 294–308, 2015
DOI: 10.1007/978-3-319-15201-1_19
Project(s): PETRA
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2017
Contribution to book
Open Access
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
Guidotti R., Monreale A., Pedreschi D., Abiteboul S.
This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Personal Analytics and Privacy, PAP 2017, held in Skopje, Macedonia, in September 2017. The 14 papers presented together with 2 invited talks in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as personal analytics, personal data mining and privacy in the context where real individual data are used for developing a data-driven service, for realizing a social study aimed at understanding nowadays society, and for publication purposes.DOI: 10.1007/978-3-319-71970-2
Project(s): SoBigData
Metrics:
Guidotti R., Monreale A., Pedreschi D., Abiteboul S.
This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Personal Analytics and Privacy, PAP 2017, held in Skopje, Macedonia, in September 2017. The 14 papers presented together with 2 invited talks in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as personal analytics, personal data mining and privacy in the context where real individual data are used for developing a data-driven service, for realizing a social study aimed at understanding nowadays society, and for publication purposes.DOI: 10.1007/978-3-319-71970-2
Project(s): SoBigData
Metrics:
See at:
ISTI Repository | doi.org | www.springer.com | CNR ExploRA
2018
Journal article
Open Access
Gastroesophageal reflux symptoms among Italian university students: epidemiology and dietary correlates using automatically recorded transactions
Martinucci I., Natilli M., Lorenzoni V., Pappalardo L., Monreale A., Turchetti G., Pedreschi D., Marchi S., Barale R., De Bortoli N.
Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal disorders worldwide, with relevant impact on the quality of life and health care costs.The aim of our study is to assess the prevalence of GERD based on self-reported symptoms among university students in central Italy. The secondary aim is to evaluate lifestyle correlates, particularly eating habits, in GERD students using automatically recorded transactions through cashiers at university canteen.Source: BMC gastroenterology (Online) 18 (2018): 116. doi:10.1186/s12876-018-0832-9
DOI: 10.1186/s12876-018-0832-9
Project(s): SoBigData
Metrics:
Martinucci I., Natilli M., Lorenzoni V., Pappalardo L., Monreale A., Turchetti G., Pedreschi D., Marchi S., Barale R., De Bortoli N.
Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal disorders worldwide, with relevant impact on the quality of life and health care costs.The aim of our study is to assess the prevalence of GERD based on self-reported symptoms among university students in central Italy. The secondary aim is to evaluate lifestyle correlates, particularly eating habits, in GERD students using automatically recorded transactions through cashiers at university canteen.Source: BMC gastroenterology (Online) 18 (2018): 116. doi:10.1186/s12876-018-0832-9
DOI: 10.1186/s12876-018-0832-9
Project(s): SoBigData
Metrics:
See at:
bmcgastroenterol.biomedcentral.com | BMC Gastroenterology | BMC Gastroenterology | BMC Gastroenterology | Archivio della ricerca della Scuola Superiore Sant'Anna | DOAJ-Articles | ISTI Repository | CNR ExploRA
2016
Journal article
Open Access
Big data research in Italy: a perspective
Bergamaschi S., Carlini E., Ceci M., Furletti B., Giannotti F., Malerba D., Mezzanzanica M., Monreale A., Pasi G., Pedreschi D., Perego R., Ruggieri S.
The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.Source: Engineering (Beijing) 2 (2016): 163–170. doi:10.1016/J.ENG.2016.02.011
DOI: 10.1016/j.eng.2016.02.011
Metrics:
Bergamaschi S., Carlini E., Ceci M., Furletti B., Giannotti F., Malerba D., Mezzanzanica M., Monreale A., Pasi G., Pedreschi D., Perego R., Ruggieri S.
The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.Source: Engineering (Beijing) 2 (2016): 163–170. doi:10.1016/J.ENG.2016.02.011
DOI: 10.1016/j.eng.2016.02.011
Metrics:
See at:
doi.org | ISTI Repository | Engineering | CNR ExploRA
2017
Contribution to book
Open Access
Personal Analytics and Privacy. An Individual and Collective Perspective
Guidotti R., Monreale A., Pedreschi D., Abiteboul S.
The First International Workshop on Personal Analytics and Privacy (PAP) was held in Skopje, Macedonia, on September 18, 2017. The purpose of the workshop is to encourage principled research that will lead to the advancement of personal data analytics, personal services development, privacy, data protection, and privacy risk assessment with the intent of bringing together researchers and practitioners interested in personal analytics and privacy. The workshop, collocated with the conference ECML/PKDD 2017, sought top-quality submissions addressing important issues related to personal analytics, personal data mining, and privacy in the context where real individual data (spatio temporal data, call details records, tweets, mobility data, transactional data, social networking data, etc.) are used for developing data-driven services, for realizing social studies aimed at understanding nowadays society, and for publication purposes.Source: 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, edited by Guidotti, R.; Monreale, A.; Pedreschi, D.; Abiteboul, S., pp. V–VI, 2017
DOI: 10.1007/978-3-319-71970-2
Project(s): SoBigData
Metrics:
Guidotti R., Monreale A., Pedreschi D., Abiteboul S.
The First International Workshop on Personal Analytics and Privacy (PAP) was held in Skopje, Macedonia, on September 18, 2017. The purpose of the workshop is to encourage principled research that will lead to the advancement of personal data analytics, personal services development, privacy, data protection, and privacy risk assessment with the intent of bringing together researchers and practitioners interested in personal analytics and privacy. The workshop, collocated with the conference ECML/PKDD 2017, sought top-quality submissions addressing important issues related to personal analytics, personal data mining, and privacy in the context where real individual data (spatio temporal data, call details records, tweets, mobility data, transactional data, social networking data, etc.) are used for developing data-driven services, for realizing social studies aimed at understanding nowadays society, and for publication purposes.Source: 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, edited by Guidotti, R.; Monreale, A.; Pedreschi, D.; Abiteboul, S., pp. V–VI, 2017
DOI: 10.1007/978-3-319-71970-2
Project(s): SoBigData
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2010
Conference article
Open Access
As time goes by: discovering eras in evolving social networks
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
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.Source: PAKSS 2010 - Advances in Knowledge Discovery and Data Mining. 14th Pacific-Asia Conference, pp. 81–90, Hyderabad, India, 21-24 June
DOI: 10.1007/978-3-642-13657-3_11
Metrics:
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
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.Source: PAKSS 2010 - Advances in Knowledge Discovery and Data Mining. 14th Pacific-Asia Conference, pp. 81–90, Hyderabad, India, 21-24 June
DOI: 10.1007/978-3-642-13657-3_11
Metrics:
See at:
www.michelecoscia.com | doi.org | www.springerlink.com | CNR ExploRA
2010
Conference article
Open Access
Exploring real mobility data with M-Atlas
Trasarti R., Rinzivillo S., Pinelli F., Nanni M., Monreale A., Renso C., Pedreschi D., Giannotti F.
Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing loca- tion aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applica- tions [3]. The M-Atlas is the evolution of the system presented in [5] allows to handle the whole knowledge discovery process from mobility data. The analysis capabilities of M-Atlas system have been applied onto a massive real life GPS dataset, obtained from 17,000 vehicles with on-board GPS receivers under a specific car insurance contract, tracked during one week of ordinary mobile activity in the urban area of the city of Milan; the dataset contains more than 2 million observations leading to a set of more than 200,000 trajectories.Source: ECML PKDD 2010 - Machine Learning and Knowledge Discovery in Databases. European Conference, pp. 624–627, Barcelona, Spain, 20-24 September 2010
DOI: 10.1007/978-3-642-15939-8_48
Metrics:
Trasarti R., Rinzivillo S., Pinelli F., Nanni M., Monreale A., Renso C., Pedreschi D., Giannotti F.
Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing loca- tion aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applica- tions [3]. The M-Atlas is the evolution of the system presented in [5] allows to handle the whole knowledge discovery process from mobility data. The analysis capabilities of M-Atlas system have been applied onto a massive real life GPS dataset, obtained from 17,000 vehicles with on-board GPS receivers under a specific car insurance contract, tracked during one week of ordinary mobile activity in the urban area of the city of Milan; the dataset contains more than 2 million observations leading to a set of more than 200,000 trajectories.Source: ECML PKDD 2010 - Machine Learning and Knowledge Discovery in Databases. European Conference, pp. 624–627, Barcelona, Spain, 20-24 September 2010
DOI: 10.1007/978-3-642-15939-8_48
Metrics:
See at:
link.springer.com | doi.org | www.springerlink.com | CNR ExploRA
2008
Conference article
Open Access
Pattern-preserving k-anonymization of sequences and its application to mobility data mining
Pensa R. G., Monreale A., Pinelli F., Pedreschi D.
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' and customers' behavior. However, this puts the citizen'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.Source: The 1st International Workshop on Privacy in Location-Based Applications, pp. 44–60, Malaga, Spain, 9 ottobre 2008
Pensa R. G., Monreale A., Pinelli F., Pedreschi D.
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' and customers' behavior. However, this puts the citizen'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.Source: The 1st International Workshop on Privacy in Location-Based Applications, pp. 44–60, Malaga, Spain, 9 ottobre 2008
See at:
sunsite.informatik.rwth-aachen.de | CNR ExploRA
2010
Conference article
Unknown
Location prediction through trajectory pattern mining
Monreale A., Pinelli F., Trasarti R., Giannotti F.
The pervasiveness of mobile devices and location based services produces as side effects an increasing volume of mobility data which in turn create the opportunity for a novel generation of analysis methods of movements behaviors. In this paper, we propose a method WhereNext aimed at predicting with a certain accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Pattern which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends by the movement of all available objects in a certain area instead by the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties emerged from the data and this allows to define matching methods strongly depending on such movement properties. Finally an exhaustive set of experiments and results on the real dataset are presented.Source: 18th Italian Symposium on Advanced Database Systems, Rimini, Italy, 20-23 June 2010
Monreale A., Pinelli F., Trasarti R., Giannotti F.
The pervasiveness of mobile devices and location based services produces as side effects an increasing volume of mobility data which in turn create the opportunity for a novel generation of analysis methods of movements behaviors. In this paper, we propose a method WhereNext aimed at predicting with a certain accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Pattern which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends by the movement of all available objects in a certain area instead by the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties emerged from the data and this allows to define matching methods strongly depending on such movement properties. Finally an exhaustive set of experiments and results on the real dataset are presented.Source: 18th Italian Symposium on Advanced Database Systems, Rimini, Italy, 20-23 June 2010
See at: CNR ExploRA
2010
Conference article
Restricted
Towards discovery of eras in social networks
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
In the last decades, much research has been devoted in topics related to Social Network Analysis. One important direction in this area is to analyze the temporal evolution of a network. So far, previous approaches analyzed this setting at both the global and the local level. In this paper, we focus on finding a way to detect temporal eras in an evolving network. We pose the basis for a general framework that aims at helping the analyst in browsing the temporal clusters both in a top-down and bottom-up way, exploring the network at any level of temporal details. We show the effectiveness of our approach to real data, by applying our proposed methodology to a co-authorship network extracted from a bibliographic dataset. Our first results are encouraging, and open the way for the definition and implementation of a general framework for discovering eras in evolving social networks.Source: Data Engineering Workshops. IEEE 26th International Conference on Data Engineering, pp. 278–281, Long Beach, USA, Febbraio 2010
DOI: 10.1109/icdew.2010.5452713
Metrics:
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
In the last decades, much research has been devoted in topics related to Social Network Analysis. One important direction in this area is to analyze the temporal evolution of a network. So far, previous approaches analyzed this setting at both the global and the local level. In this paper, we focus on finding a way to detect temporal eras in an evolving network. We pose the basis for a general framework that aims at helping the analyst in browsing the temporal clusters both in a top-down and bottom-up way, exploring the network at any level of temporal details. We show the effectiveness of our approach to real data, by applying our proposed methodology to a co-authorship network extracted from a bibliographic dataset. Our first results are encouraging, and open the way for the definition and implementation of a general framework for discovering eras in evolving social networks.Source: Data Engineering Workshops. IEEE 26th International Conference on Data Engineering, pp. 278–281, Long Beach, USA, Febbraio 2010
DOI: 10.1109/icdew.2010.5452713
Metrics:
See at:
doi.org | CNR ExploRA
2010
Conference article
Open Access
Preserving privacy in semantic-rich trajectories of human mobility
Monreale A., Trasarti R., Renso C., Pedreschi D., Bogorny V.
The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for an- alyzing 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 pri- vacy 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 in- ferring that a given person, observed in a sequence of non- sensitive 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 trajec- tory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.Source: 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, pp. 47, San Jose, CA, USA, 3-5 November 2010
DOI: 10.1145/1868470.1868481
Metrics:
Monreale A., Trasarti R., Renso C., Pedreschi D., Bogorny V.
The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for an- alyzing 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 pri- vacy 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 in- ferring that a given person, observed in a sequence of non- sensitive 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 trajec- tory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.Source: 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, pp. 47, San Jose, CA, USA, 3-5 November 2010
DOI: 10.1145/1868470.1868481
Metrics:
See at:
www.inf.ufsc.br | dl.acm.org | doi.org | CNR ExploRA
2010
Conference article
Unknown
Discovering Eras in Evolving Social Networks
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
An important topic in complex network research is the temporal evolution of networks. Existing approaches aim at analyzing the evolution extracting properties of either the entire network or local patterns. In this paper, we focus 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 between two temporal snapshots of the network. We devise a framework to discover and browse the eras, 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; 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.Source: 18th Italian Symposium on Advanced Database Systems, pp. 78–85, Rimini, Italy, 20-23 June 2010
Berlingerio M., Coscia M., Giannotti F., Monreale A., Pedreschi D.
An important topic in complex network research is the temporal evolution of networks. Existing approaches aim at analyzing the evolution extracting properties of either the entire network or local patterns. In this paper, we focus 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 between two temporal snapshots of the network. We devise a framework to discover and browse the eras, 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; 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.Source: 18th Italian Symposium on Advanced Database Systems, pp. 78–85, Rimini, Italy, 20-23 June 2010
See at: CNR ExploRA
2010
Report
Unknown
Towards anonymous semantic trajectories
Monreale A., Trasarti R., Renso C., Bogorny V., Pedreschi D.
In recent years, spatio-temporal and moving objects databases have gained consi-derable interest, due to the diffusion of mobile devices and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Recent advances in spatio-temporal data analysis focused on the semantic aspects of the movement data, thus leading to the definition of semantic trajectory concept. However, the analysis of this kind of data can compromise the privacy of users because the location data allows inferences which may help an attacker to discovery personal and sensitive information, like habits and preferences of individuals. In this paper we briefly present an approach for the generalization of semantic tra-jectories that can be adopted for obtaining datasets satisfying the k-anonymity property; specifically, this method exploits ontologies to realize a framework for publishing semantic trajectories while preserving privacy of the tracked users. We show that this generalization method is able to preserve the semantic tagging obtained by the analysis of the resulting dataset.Source: ISTI Technical reports, 2010
Monreale A., Trasarti R., Renso C., Bogorny V., Pedreschi D.
In recent years, spatio-temporal and moving objects databases have gained consi-derable interest, due to the diffusion of mobile devices and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Recent advances in spatio-temporal data analysis focused on the semantic aspects of the movement data, thus leading to the definition of semantic trajectory concept. However, the analysis of this kind of data can compromise the privacy of users because the location data allows inferences which may help an attacker to discovery personal and sensitive information, like habits and preferences of individuals. In this paper we briefly present an approach for the generalization of semantic tra-jectories that can be adopted for obtaining datasets satisfying the k-anonymity property; specifically, this method exploits ontologies to realize a framework for publishing semantic trajectories while preserving privacy of the tracked users. We show that this generalization method is able to preserve the semantic tagging obtained by the analysis of the resulting dataset.Source: ISTI Technical reports, 2010
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2013
Contribution to book
Restricted
Anonymity: a comparison between the legal and computer science perspectives
Mascetti S., Monreale A., Ricci A., Gerino A.
Privacy preservation has emerged as a major challenge in ICT. One possible solution for enforcing privacy is to guarantee anonymity. Indeed, ac- cording 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 re- quirements. 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 high- lighting the need for a joint research effort.Source: European Data Protection: Coming of Age, edited by Serge Gutwirth, Ronald Leenes, Paul De Hert, Yves Poullet, pp. 85–115, 2013
DOI: 10.1007/978-94-007-5170-5_4
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Mascetti S., Monreale A., Ricci A., Gerino A.
Privacy preservation has emerged as a major challenge in ICT. One possible solution for enforcing privacy is to guarantee anonymity. Indeed, ac- cording 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 re- quirements. 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 high- lighting the need for a joint research effort.Source: European Data Protection: Coming of Age, edited by Serge Gutwirth, Ronald Leenes, Paul De Hert, Yves Poullet, pp. 85–115, 2013
DOI: 10.1007/978-94-007-5170-5_4
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doi.org | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | link.springer.com | CNR ExploRA
2014
Journal article
Open Access
Privacy-by-design in big data analytics and social mining
Monreale A., Rinzivillo S., Pratesi F., Giannotti F., Pedreschi D.
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.Source: EPJ 3 (2014). doi:10.1140/epjds/s13688-014-0010-4
DOI: 10.1140/epjds/s13688-014-0010-4
Project(s): DATA SIM, PETRA
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Monreale A., Rinzivillo S., Pratesi F., Giannotti F., Pedreschi D.
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.Source: EPJ 3 (2014). doi:10.1140/epjds/s13688-014-0010-4
DOI: 10.1140/epjds/s13688-014-0010-4
Project(s): DATA SIM
, PETRA Metrics:
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EPJ Data Science | EPJ Data Science | www.epjdatascience.com | CNR ExploRA
2014
Conference article
Open Access
A privacy risk model for trajectory data
Basu A., Monreale A., Corena J. C., Giannotti F., Pedreschi D., Kiyomoto S., Miyake Y., Yanagihara T., Trasarti R.
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 privacypreserving 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 worstcase 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.Source: Trust Management VIII. 8th IFIP WG 11.11 International Conference (IFIPTM 2014), pp. 125–140, Singapore, 07-10/07/2014
DOI: 10.1007/978-3-662-43813-8_9
Metrics:
Basu A., Monreale A., Corena J. C., Giannotti F., Pedreschi D., Kiyomoto S., Miyake Y., Yanagihara T., Trasarti R.
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 privacypreserving 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 worstcase 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.Source: Trust Management VIII. 8th IFIP WG 11.11 International Conference (IFIPTM 2014), pp. 125–140, Singapore, 07-10/07/2014
DOI: 10.1007/978-3-662-43813-8_9
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link.springer.com | doi.org | Hyper Article en Ligne | link.springer.com | www.scopus.com | CNR ExploRA
2014
Conference article
Restricted
Fair pattern discovery
Hajian S., Monreale A., Pedreschi D., Domingo-Ferrer J., Giannotti F.
Data mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. We are assisting to unprecedented opportunities of understanding human and society behavior that unfortunately is darkened by several risks for human rights: one of this is the unfair discrimination based on the extracted patterns and profiles. Consider the case when a set of patterns extracted from the personal data of a population of individual persons is released for subsequent use in a decision making process, such as, e.g., granting or denying credit. Decision rules based on such patterns may lead to unfair discrimination, depending on what is represented in the training cases. In this context, we address the discrimination risks resulting from publishing frequent patterns. We present a set of pattern sanitization methods, one for each discrimination measure used in the legal literature, for fair (discrimination-protected) publishing of frequent pattern mining results. Our proposed pattern sanitization methods yield discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. Finally, the effectiveness of our proposals is assessed by extensive experiments. Copyright 2014 ACM.Source: 29th Annual ACM Symposium on Applied Computing (SAC'14), pp. 113–120, Gyeongju, Republic of Korea, 24-28/03/2014
DOI: 10.1145/2554850.2555043
Metrics:
Hajian S., Monreale A., Pedreschi D., Domingo-Ferrer J., Giannotti F.
Data mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. We are assisting to unprecedented opportunities of understanding human and society behavior that unfortunately is darkened by several risks for human rights: one of this is the unfair discrimination based on the extracted patterns and profiles. Consider the case when a set of patterns extracted from the personal data of a population of individual persons is released for subsequent use in a decision making process, such as, e.g., granting or denying credit. Decision rules based on such patterns may lead to unfair discrimination, depending on what is represented in the training cases. In this context, we address the discrimination risks resulting from publishing frequent patterns. We present a set of pattern sanitization methods, one for each discrimination measure used in the legal literature, for fair (discrimination-protected) publishing of frequent pattern mining results. Our proposed pattern sanitization methods yield discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. Finally, the effectiveness of our proposals is assessed by extensive experiments. Copyright 2014 ACM.Source: 29th Annual ACM Symposium on Applied Computing (SAC'14), pp. 113–120, Gyeongju, Republic of Korea, 24-28/03/2014
DOI: 10.1145/2554850.2555043
Metrics:
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dl.acm.org | doi.org | CNR ExploRA