2008
Conference article
Restricted
Clustering of German municipalities based on mobility characteristics
Zanda A., Koerner C., Giannotti F., Schulz D., May M.
This paper presents a clustering approach which groups German municipalities according to mobility characteristics. As the number of measurements for nationwide mobility studies is usually restricted, this clustering provides a means to infer mobility information for locations without measurements based on values of their respective cluster representatives. Our approach considers local and global information, i.e. characteristics of municipalities as well as relationships between municipalities. We realize previous findings in urban geography by using techniques from graph theory and computer vision. Our clustering consists of a two-step model, which rst extracts and condenses single mobility characteristics and subsequently combines the various features. We apply our model to all German municipalities between 10,000 and 50,000 inhabitants. The clustering has been successfully applied in practice for the inference of traffic frequencies.Source: 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 479–482, Irvine, CA, US, 5-7 novembre 2008
DOI: 10.1145/1463434.1463514
Metrics:
Zanda A., Koerner C., Giannotti F., Schulz D., May M.
This paper presents a clustering approach which groups German municipalities according to mobility characteristics. As the number of measurements for nationwide mobility studies is usually restricted, this clustering provides a means to infer mobility information for locations without measurements based on values of their respective cluster representatives. Our approach considers local and global information, i.e. characteristics of municipalities as well as relationships between municipalities. We realize previous findings in urban geography by using techniques from graph theory and computer vision. Our clustering consists of a two-step model, which rst extracts and condenses single mobility characteristics and subsequently combines the various features. We apply our model to all German municipalities between 10,000 and 50,000 inhabitants. The clustering has been successfully applied in practice for the inference of traffic frequencies.Source: 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 479–482, Irvine, CA, US, 5-7 novembre 2008
DOI: 10.1145/1463434.1463514
Metrics:
See at:
doi.org 
| CNR ExploRA
2006
Software
Unknown
k-Privacy
Carfì D., Atzori M., Giannotti F.
k-PRIVACY is a Java implementation of the well-known Datafly anonymization algorithm. It enforces anonymity of tuples in a given (private) table by generalizing and suppressing some tuples. k-PRIVACY protects from linking attacks by providing a k-anonymous (public) table that can be safely shared. It comes with both a graphic user interface and a command line console. Please refer to work on k-anonymity for details on this privacy-preserving technology.
Carfì D., Atzori M., Giannotti F.
k-PRIVACY is a Java implementation of the well-known Datafly anonymization algorithm. It enforces anonymity of tuples in a given (private) table by generalizing and suppressing some tuples. k-PRIVACY protects from linking attacks by providing a k-anonymous (public) table that can be safely shared. It comes with both a graphic user interface and a command line console. Please refer to work on k-anonymity for details on this privacy-preserving technology.
See at: CNR ExploRA
2007
Other
Unknown
See at: CNR ExploRA
2009
Conference article
Restricted
Movement data anonymity through generalization.
Andrienko G., Andrienko N., Giannotti F., Monreale A., Pedreschi D.
In recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the diusion of mobile devices (e.g., mobile phones, RFID devices and GPS devices) and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Clearly, in these applications privacy is a concern,since models extracted from this kind of data can reveal the behavior of group of individuals, thus compromising their privacy. Movement data present a new challenge for the privacy-preserving data mining community because of their spatial and temporal characteristics. In this position paper we brie y present an approach for the generalization of movement data that can be adopted for obtaining k-anonymity in spatio-temporal datasets; specif- ically, it can be used to realize a framework for publishing of spatio-temporal data while preserving privacy. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.Source: 2nd SIGSPATIAL ACM GIS 2009. International Workshop on Security and Privacy in GIS and LBS, pp. 27, Seattle, Washington, 4-6 November 2009
DOI: 10.1145/1667502.1667510
Metrics:
Andrienko G., Andrienko N., Giannotti F., Monreale A., Pedreschi D.
In recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the diusion of mobile devices (e.g., mobile phones, RFID devices and GPS devices) and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Clearly, in these applications privacy is a concern,since models extracted from this kind of data can reveal the behavior of group of individuals, thus compromising their privacy. Movement data present a new challenge for the privacy-preserving data mining community because of their spatial and temporal characteristics. In this position paper we brie y present an approach for the generalization of movement data that can be adopted for obtaining k-anonymity in spatio-temporal datasets; specif- ically, it can be used to realize a framework for publishing of spatio-temporal data while preserving privacy. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.Source: 2nd SIGSPATIAL ACM GIS 2009. International Workshop on Security and Privacy in GIS and LBS, pp. 27, Seattle, Washington, 4-6 November 2009
DOI: 10.1145/1667502.1667510
Metrics:
See at:
dl.acm.org | doi.org | CNR ExploRA
2002
Report
Unknown
Characterizing Web User Accesses: a transactional approach to Web Log Clustering
Giannotti F., Gozzi C., Manco G.
An abstract is not availableSource: ISTI Technical reports, 2002
Giannotti F., Gozzi C., Manco G.
An abstract is not availableSource: ISTI Technical reports, 2002
See at: CNR ExploRA
2002
Report
Unknown
Clustering transactional data
Giannotti F., Gozzi C., Manco G.
This paper presents a partitioned method which crosses the limitations of traditional approaches to clustering of transactional data. A modification of the stanard K-Means algorithm is presented, which has a good scalability on the number of objects and attributes, but can only work with numeric vectors of fixed length.Source: ISTI Technical reports, 2002
Giannotti F., Gozzi C., Manco G.
This paper presents a partitioned method which crosses the limitations of traditional approaches to clustering of transactional data. A modification of the stanard K-Means algorithm is presented, which has a good scalability on the number of objects and attributes, but can only work with numeric vectors of fixed length.Source: ISTI Technical reports, 2002
See at: CNR ExploRA
2002
Report
Unknown
Software and tools for transactional clustering
Giannotti F., Gozzi C., Manco G.
An abstract is not availableSource: ISTI Technical reports, 2002
Giannotti F., Gozzi C., Manco G.
An abstract is not availableSource: ISTI Technical reports, 2002
See at: CNR ExploRA
2010
Journal article
Open Access
Hiding Sequential and Spatiotemporal Patterns
Giannotti F., Bonchi F., Abul O.
The process of discovering relevant patterns holding in a database was first indicated as a threat to database security by O'Leary in [1]. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent item sets mining. Following many real-world data and application demands, in this paper, we shift the problem of knowledge hiding to contexts where both the data and the extracted knowledge have a sequential structure. We define the problem of hiding sequential patterns and show its NP-hardness. Thus, we devise heuristics and a polynomial sanitization algorithm. Starting from this framework, we specialize it to the more complex case of spatiotemporal patterns extracted from moving objects databases. Finally, we discuss a possible kind of attack to our model, which exploits the knowledge of the underlying road network, and enhance our model to protect from this kind of attack. An exhaustive experiential analysis on real-world data sets shows the effectiveness of our proposal.Source: IEEE transactions on knowledge and data engineering (Print) 22 (2010): 1709–1723. doi:10.1109/TKDE.2009.213
DOI: 10.1109/tkde.2009.213
Metrics:
Giannotti F., Bonchi F., Abul O.
The process of discovering relevant patterns holding in a database was first indicated as a threat to database security by O'Leary in [1]. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent item sets mining. Following many real-world data and application demands, in this paper, we shift the problem of knowledge hiding to contexts where both the data and the extracted knowledge have a sequential structure. We define the problem of hiding sequential patterns and show its NP-hardness. Thus, we devise heuristics and a polynomial sanitization algorithm. Starting from this framework, we specialize it to the more complex case of spatiotemporal patterns extracted from moving objects databases. Finally, we discuss a possible kind of attack to our model, which exploits the knowledge of the underlying road network, and enhance our model to protect from this kind of attack. An exhaustive experiential analysis on real-world data sets shows the effectiveness of our proposal.Source: IEEE transactions on knowledge and data engineering (Print) 22 (2010): 1709–1723. doi:10.1109/TKDE.2009.213
DOI: 10.1109/tkde.2009.213
Metrics:
See at:
Aperta - TÜBİTAK Açık Arşivi 
| IEEE Transactions on Knowledge and Data Engineering | ieeexplore.ieee.org | CNR ExploRA
2011
Conference article
Unknown
Mobility, data mining and privacy: mining human movement patterns from trajectory data
Giannotti, Fosca
Source: Extraction et gestion des connaissances, ECG 2011, pp. 5–6, Brest-France, 25-28 January 2011
Giannotti, Fosca
Source: Extraction et gestion des connaissances, ECG 2011, pp. 5–6, Brest-France, 25-28 January 2011
See at: CNR ExploRA | www.editions-hermann.fr
2011
Conference article
Unknown
Privacy-Preserving Data Mining from Outsourced Databases
Giannotti Fosca, Lakshmanan Laks V. S., Monreale Anna, Pedreschi Dino, Wang Hui
Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service: a company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the outsourced database and the knowledge extract from it by data mining are considered private property of the data owner. To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing a data mining task within a corporate privacy-preserving framework. We propose a scheme for privacy-preserving outsourced mining which offers a formal protection against information disclosure, and show that the data owner can recover the correct data mining results efficiently.Source: Computers, Privacy and Data Protection: an Element of Choice, pp. 411–426, Bruxelles, Gennaio 2010
DOI: 10.1007/978-94-007-0641-5_19
Metrics:
Giannotti Fosca, Lakshmanan Laks V. S., Monreale Anna, Pedreschi Dino, Wang Hui
Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service: a company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the outsourced database and the knowledge extract from it by data mining are considered private property of the data owner. To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing a data mining task within a corporate privacy-preserving framework. We propose a scheme for privacy-preserving outsourced mining which offers a formal protection against information disclosure, and show that the data owner can recover the correct data mining results efficiently.Source: Computers, Privacy and Data Protection: an Element of Choice, pp. 411–426, Bruxelles, Gennaio 2010
DOI: 10.1007/978-94-007-0641-5_19
Metrics:
See at:
doi.org | CNR ExploRA
2011
Conference article
Restricted
Mobility, data mining and privacy understanding human movement patterns from trajectory data
Giannotti, Fosca
The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data, such as mobile phone call records and GPS tracks. This is a scenario of great opportunities and risks: on one side, mining this data can produce useful knowledge, supporting sustainable mobility and intelligent transportation systems; on the other side, individual privacy is at risk, as the mobility data contain sensitive personal information. A new multidisciplinary research area is emerging at this crossroads of mobility, data mining, and privacy. The talk assesses this research frontier from a data mining perspective, and illustrates the results of a European-wide research project called GeoPKDD, Geographic Privacy-Aware Knowledge Discovery and Delivery. GeoPKDD has created an integrated platform named M-ATLAS for complex analysis of mobility data, which combines spatio-temporal querying capabilities with data mining, visual analytics and semantic technologies, thus providing a full support for the Mobility Knowledge Discovery process. In this talk, we focus on the key data mining models: trajectory patterns and trajectory clustering, and illustrate the analytical power of our system in unvealing the complexity of urban mobility in a large metropolitan area by means of a large scale experiment, based on a massive real life GPS dataset, obtained from 17,000 vehicles with on-board GPS receivers, tracked during one week of ordinary mobile activity in the urban area of the city of Milan, Italy.Source: 2011 IEEE 12th International Conference on Mobile Data Management, MDM 2011, pp. 4–5, Lulea, Sweden, 6-9 June 2011
DOI: 10.1109/mdm.2011.103
Metrics:
Giannotti, Fosca
The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data, such as mobile phone call records and GPS tracks. This is a scenario of great opportunities and risks: on one side, mining this data can produce useful knowledge, supporting sustainable mobility and intelligent transportation systems; on the other side, individual privacy is at risk, as the mobility data contain sensitive personal information. A new multidisciplinary research area is emerging at this crossroads of mobility, data mining, and privacy. The talk assesses this research frontier from a data mining perspective, and illustrates the results of a European-wide research project called GeoPKDD, Geographic Privacy-Aware Knowledge Discovery and Delivery. GeoPKDD has created an integrated platform named M-ATLAS for complex analysis of mobility data, which combines spatio-temporal querying capabilities with data mining, visual analytics and semantic technologies, thus providing a full support for the Mobility Knowledge Discovery process. In this talk, we focus on the key data mining models: trajectory patterns and trajectory clustering, and illustrate the analytical power of our system in unvealing the complexity of urban mobility in a large metropolitan area by means of a large scale experiment, based on a massive real life GPS dataset, obtained from 17,000 vehicles with on-board GPS receivers, tracked during one week of ordinary mobile activity in the urban area of the city of Milan, Italy.Source: 2011 IEEE 12th International Conference on Mobile Data Management, MDM 2011, pp. 4–5, Lulea, Sweden, 6-9 June 2011
DOI: 10.1109/mdm.2011.103
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2011
Report
Open Access
Quality assurance of outsourced outlier mining
Zhang Yongjin, Liu Ruilin, Wang Hui Wendy, Monreale Anna, Pedreschi Dino, Giannotti Fosca, Guo Wenge
Spurred by developments such as in cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third-party service provider. However, as the service providers may not be fully trusted, a dishonest service provider may return inaccurate mining results to the database owner. In this paper, we study the problem of providing quality assurance for outsourced outlier mining. We propose an efficient and practical auditing approach that can verify (1) whether the service provider returns the outliers originated from the hosted database, and (2) whether the service provider returns correct and complete outlier mining results. The key of our approach is to insert a small amount of artificial tuples into the outsourced database; the mining results of the service provider will be audited by analyzing the inserted tuples in the returned results with probabilistic guarantee. Our empirical results demonstrate the effectiveness and efficiency of our method.Source: ISTI Technical reports, 2011
Zhang Yongjin, Liu Ruilin, Wang Hui Wendy, Monreale Anna, Pedreschi Dino, Giannotti Fosca, Guo Wenge
Spurred by developments such as in cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third-party service provider. However, as the service providers may not be fully trusted, a dishonest service provider may return inaccurate mining results to the database owner. In this paper, we study the problem of providing quality assurance for outsourced outlier mining. We propose an efficient and practical auditing approach that can verify (1) whether the service provider returns the outliers originated from the hosted database, and (2) whether the service provider returns correct and complete outlier mining results. The key of our approach is to insert a small amount of artificial tuples into the outsourced database; the mining results of the service provider will be audited by analyzing the inserted tuples in the returned results with probabilistic guarantee. Our empirical results demonstrate the effectiveness and efficiency of our method.Source: ISTI Technical reports, 2011
See at:
ISTI Repository | CNR ExploRA
2012
Conference article
Restricted
Privacy-preserving mining of association rules from outsourced transaction databases. Extended abstract
Giannotti F., Lakshmanan L. V., Monreale A., Pedreschi D., Wang H.
Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the corporation (data owner). To protect corporate privacy,the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose a scheme for privacy preserving outsourced mining and show that the owner can recover the true patterns as well as their support by maintaining a compact synopsis.Source: Twentieth Italian Symposium on Advanced Database Systems, pp. 233–242, Venice, Italy, 24-27 June 2012
Giannotti F., Lakshmanan L. V., Monreale A., Pedreschi D., Wang H.
Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the corporation (data owner). To protect corporate privacy,the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose a scheme for privacy preserving outsourced mining and show that the owner can recover the true patterns as well as their support by maintaining a compact synopsis.Source: Twentieth Italian Symposium on Advanced Database Systems, pp. 233–242, Venice, Italy, 24-27 June 2012
See at:
sebd2012.dei.unipd.it | CNR ExploRA
2012
Journal article
Open Access
A planetary nervous system for social mining and collective awareness
Giannotti F., Pedreschi D., Pentland A., Lukowicz P., Kossmann D., Crowley J., Helbing D.
We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Source: The European physical journal. Special topics (Online) 214 (2012): 49–75. doi:10.1140/epjst/e2012-01688-9
DOI: 10.1140/epjst/e2012-01688-9
DOI: 10.3929/ethz-b-000061808
DOI: 10.48550/arxiv.1304.3700
Project(s): FUTURICT
Metrics:
Giannotti F., Pedreschi D., Pentland A., Lukowicz P., Kossmann D., Crowley J., Helbing D.
We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Source: The European physical journal. Special topics (Online) 214 (2012): 49–75. doi:10.1140/epjst/e2012-01688-9
DOI: 10.1140/epjst/e2012-01688-9
DOI: 10.3929/ethz-b-000061808
DOI: 10.48550/arxiv.1304.3700
Project(s): FUTURICT
Metrics:
See at:
arXiv.org e-Print Archive | RERO DOC Digital Library | The European Physical Journal Special Topics | DSpace@MIT | The European Physical Journal Special Topics | ETH Zürich Research Collection | doi.org | INRIA a CCSD electronic archive server | link.springer.com | CNR ExploRA
2012
Journal article
Open Access
Smart cities of the future
Batty M., Axhausen K. W., Giannotti F., Pozdnoukhov A., Bazzani A., Wachowicz M., Ouzounis G., Portugali Y.
Here we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for urban populations. We begin by defining the state of the art, explaining the science of smart cities. We define six scenarios based on new cities badging themselves as smart, older cities regenerating themselves as smart, the development of science parks, tech cities, and technopoles focused on high technologies, the development of urban services using contemporary ICT, the use of ICT to develop new urban intelligence functions, and the development of online and mobile forms of participation. Seven project areas are then proposed: Integrated Databases for the Smart City, Sensing, Networking and the Impact of New Social Media, Modelling Network Performance, Mobility and Travel Behaviour, Modelling Urban Land Use, Transport and Economic Interactions, Modelling Urban Transactional Activities in Labour and Housing Markets, Decision Support as Urban Intelligence, Participatory Governance and Planning Structures for the Smart City. Finally we anticipate the paradigm shifts that will occur in this research and define a series of key demonstrators which we believe are important to progressing a science of smart cities.Source: The European physical journal. Special topics 214 (2012): 481–518. doi:10.1140/epjst/e2012-01703-3
DOI: 10.1140/epjst/e2012-01703-3
DOI: 10.3929/ethz-b-000061793
Project(s): FUTURICT
Metrics:
Batty M., Axhausen K. W., Giannotti F., Pozdnoukhov A., Bazzani A., Wachowicz M., Ouzounis G., Portugali Y.
Here we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for urban populations. We begin by defining the state of the art, explaining the science of smart cities. We define six scenarios based on new cities badging themselves as smart, older cities regenerating themselves as smart, the development of science parks, tech cities, and technopoles focused on high technologies, the development of urban services using contemporary ICT, the use of ICT to develop new urban intelligence functions, and the development of online and mobile forms of participation. Seven project areas are then proposed: Integrated Databases for the Smart City, Sensing, Networking and the Impact of New Social Media, Modelling Network Performance, Mobility and Travel Behaviour, Modelling Urban Land Use, Transport and Economic Interactions, Modelling Urban Transactional Activities in Labour and Housing Markets, Decision Support as Urban Intelligence, Participatory Governance and Planning Structures for the Smart City. Finally we anticipate the paradigm shifts that will occur in this research and define a series of key demonstrators which we believe are important to progressing a science of smart cities.Source: The European physical journal. Special topics 214 (2012): 481–518. doi:10.1140/epjst/e2012-01703-3
DOI: 10.1140/epjst/e2012-01703-3
DOI: 10.3929/ethz-b-000061793
Project(s): FUTURICT
Metrics:
See at:
The European Physical Journal Special Topics | The European Physical Journal Special Topics | ETH Zürich Research Collection | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | link.springer.com | CNR ExploRA
2012
Journal article
Open Access
FuturICT - The road towards ethical ICT
Van Den Hoven J., Helbing D., Pedreschi D., Domingo-Ferrer J., Giannotti F., Christen M.
The pervasive use of information and communication technology (ICT) in modern societies enables countless opportunities for individuals, institutions, businesses and scientists, but also raises difficult ethical and social problems. In particular, ICT helped to make societies more complex and thus harder to understand, which impedes social and political interventions to avoid harm and to increase the common good. To overcome this obstacle, the large-scale EU flagship proposal FuturICT intends to create a platform for accessing global human knowledge as a public good and instruments to increase our understanding of the information society by making use of ICT-based research. In this contribution, we outline the ethical justification for such an endeavor. We argue that the ethical issues raised by FuturICT research projects overlap substantially with many of the known ethical problems emerging from ICT use in general. By referring to the notion of Value Sensitive Design, we show for the example of privacy how this core value of responsible ICT can be protected in pursuing research in the framework of FuturICT. In addition, we discuss further ethical issues and outline the institutional design of FuturICT allowing to address them.Source: The European physical journal. Special topics (Online) 214 (2012): 153–181. doi:10.1140/epjst/e2012-01691-2
DOI: 10.1140/epjst/e2012-01691-2
DOI: 10.48550/arxiv.1210.8181
Project(s): FUTURICT
Metrics:
Van Den Hoven J., Helbing D., Pedreschi D., Domingo-Ferrer J., Giannotti F., Christen M.
The pervasive use of information and communication technology (ICT) in modern societies enables countless opportunities for individuals, institutions, businesses and scientists, but also raises difficult ethical and social problems. In particular, ICT helped to make societies more complex and thus harder to understand, which impedes social and political interventions to avoid harm and to increase the common good. To overcome this obstacle, the large-scale EU flagship proposal FuturICT intends to create a platform for accessing global human knowledge as a public good and instruments to increase our understanding of the information society by making use of ICT-based research. In this contribution, we outline the ethical justification for such an endeavor. We argue that the ethical issues raised by FuturICT research projects overlap substantially with many of the known ethical problems emerging from ICT use in general. By referring to the notion of Value Sensitive Design, we show for the example of privacy how this core value of responsible ICT can be protected in pursuing research in the framework of FuturICT. In addition, we discuss further ethical issues and outline the institutional design of FuturICT allowing to address them.Source: The European physical journal. Special topics (Online) 214 (2012): 153–181. doi:10.1140/epjst/e2012-01691-2
DOI: 10.1140/epjst/e2012-01691-2
DOI: 10.48550/arxiv.1210.8181
Project(s): FUTURICT
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arXiv.org e-Print Archive | The European Physical Journal Special Topics | Recolector de Ciencia Abierta, RECOLECTA | The European Physical Journal Special Topics | NARCIS | Zurich Open Repository and Archive | doi.org | link.springer.com | CNR ExploRA
2012
Conference article
Open Access
Towards democratic group detection in complex networks.
Coscia M., Giannotti F., Pedreschi D.
To detect groups in networks is an interesting problem with applications in social and security analysis. Many large networks lack a global community organization. In these cases, traditional partitioning algorithms fail to detect a hidden modular structure, assuming a global modular organization. We define a prototype for a simple localfirst approach to community discovery, namely the democratic vote of each node for the communities in its ego neighborhood. We create a preliminary test of this intuition against the state-of-the-art community discovery methods, and find that our new method outperforms them in the quality of the obtained groups, evaluated using metadata of two real world networks. We give also the intuition of the incremental nature and the limited time complexity of the proposed algorithm.Source: Social Computing, Behavioral - Cultural Modeling and Prediction. 5th International Conference, pp. 105–113, College Park, MD, USA, 3-5 April 2012
DOI: 10.1007/978-3-642-29047-3_13
Metrics:
Coscia M., Giannotti F., Pedreschi D.
To detect groups in networks is an interesting problem with applications in social and security analysis. Many large networks lack a global community organization. In these cases, traditional partitioning algorithms fail to detect a hidden modular structure, assuming a global modular organization. We define a prototype for a simple localfirst approach to community discovery, namely the democratic vote of each node for the communities in its ego neighborhood. We create a preliminary test of this intuition against the state-of-the-art community discovery methods, and find that our new method outperforms them in the quality of the obtained groups, evaluated using metadata of two real world networks. We give also the intuition of the incremental nature and the limited time complexity of the proposed algorithm.Source: Social Computing, Behavioral - Cultural Modeling and Prediction. 5th International Conference, pp. 105–113, College Park, MD, USA, 3-5 April 2012
DOI: 10.1007/978-3-642-29047-3_13
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www.michelecoscia.com | doi.org | link.springer.com | CNR ExploRA
2012
Conference article
Open Access
AUDIO: an integrity auditing framework of outlier-mining-as-a-service systems
Liu R., Wang W. H., Monreale A., Pedreschi D., Giannotti F., Wenge G.
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'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.Source: Machine Learning and Knowledge Discovery in Databases European Conference, pp. 1–18, Bristol, UK, 24-28 September 2012
DOI: 10.1007/978-3-642-33486-3_1
Metrics:
Liu R., Wang W. H., Monreale A., Pedreschi D., Giannotti F., Wenge G.
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'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.Source: Machine Learning and Knowledge Discovery in Databases European Conference, pp. 1–18, Bristol, UK, 24-28 September 2012
DOI: 10.1007/978-3-642-33486-3_1
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link.springer.com | doi.org | www.springerlink.com | CNR ExploRA
2012
Conference article
Restricted
Injecting discrimination and privacy awareness into pattern discovery
Hajian S., Monreale A., Pedreschi D., Domingo F. 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. Data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a combined pattern sanitization framework that yields both privacy and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion.Source: IEEE, 12th International Conference on Data Mining Workshops, ICDMW 2012., pp. 360–369, Brussels, 10 December 2012
DOI: 10.1109/icdmw.2012.51
Metrics:
Hajian S., Monreale A., Pedreschi D., Domingo F. 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. Data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a combined pattern sanitization framework that yields both privacy and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion.Source: IEEE, 12th International Conference on Data Mining Workshops, ICDMW 2012., pp. 360–369, Brussels, 10 December 2012
DOI: 10.1109/icdmw.2012.51
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doi.org | CNR ExploRA