Elena Ferrari - Böcker

Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results—they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. Divided into seven parts, the book provides in-depth coverage of the most novel reference scenarios for privacy-preserving techniques. The first part gives general techniques that can be applied to various applications discussed in the rest of the book. The second section focuses on the sanitization of network traces and privacy in data stream mining. After the third part on privacy in spatio-temporal data mining and mobility data analysis, the book examines time series analysis in the fourth section, explaining how a perturbation method and a segment-based method can tackle privacy issues of time series data. The fifth section on biomedical data addresses genomic data as well as the problem of privacy-aware information sharing of health data. In the sixth section on web applications, the book deals with query log mining and web recommender systems. The final part on social networks analyzes privacy issues related to the management of social network data under different perspectives.While several new results have recently occurred in the privacy, database, and data mining research communities, a uniform presentation of up-to-date techniques and applications is lacking. Filling this void, Privacy-Aware Knowledge Discovery presents novel algorithms, patterns, and models, along with a significant collection of open problems for future investigation.

Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results—they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. Divided into seven parts, the book provides in-depth coverage of the most novel reference scenarios for privacy-preserving techniques. The first part gives general techniques that can be applied to various applications discussed in the rest of the book. The second section focuses on the sanitization of network traces and privacy in data stream mining. After the third part on privacy in spatio-temporal data mining and mobility data analysis, the book examines time series analysis in the fourth section, explaining how a perturbation method and a segment-based method can tackle privacy issues of time series data. The fifth section on biomedical data addresses genomic data as well as the problem of privacy-aware information sharing of health data. In the sixth section on web applications, the book deals with query log mining and web recommender systems. The final part on social networks analyzes privacy issues related to the management of social network data under different perspectives.While several new results have recently occurred in the privacy, database, and data mining research communities, a uniform presentation of up-to-date techniques and applications is lacking. Filling this void, Privacy-Aware Knowledge Discovery presents novel algorithms, patterns, and models, along with a significant collection of open problems for future investigation.

Table of Contents: Access Control: Basic Concepts / Discretionary Access Control for Relational Data Management Systems / Discretionary Access Control for Advanced Data Models / Mandatory Access Control / Role-based Access Control / Emerging Trends in Access Control

(ii) controlled information sharing, because in OSNs, where personal information is not only connected to user profiles, but spans across users' social activities and interactions, users must be provided with the possibility to directly control information flows;

This book constitutes the refereed proceedings of the 9th International Conference on Extending Database Technology, EDBT 2004, held in Heraklion, Crete, Greece, in March 2004. The 42 revised full papers presented together with 2 industrial application papers, 15 software demos, and 3 invited contributions were carefully reviewed and selected from 294 submissions. The papers are organized in topical sections on distributed, mobile and peer-to-peer database systems; data mining and knowledge discovery; trustworthy database systems; innovative query processing techniques for XML data; data and information on the web; query processing techniques for spatial databases; foundations of query processing; advanced query processing and optimization; query processing techniques for data and schemas; multimedia and quality-aware systems; indexing techniques; and imprecise sequence pattern queries.

Spatial data is essential in a wide range of application domains today. While geographical applications remain the key target area, spatial properties are required in other contexts such as computer-aided design, robotics and image processing. Associated with these is the constantly growing number of distributed processing architectures, based on, for example, grid systems, sensor data networks, and personalized smart devices.Spatial Data on the Web links these two research streams, focusing on the modeling and management of spatial data in distributed systems. Belussi and his coeditors have structured the contributions from internationally renowned researchers into four parts. Part I presents models for representing semistructured, multiresolution and multiscale data; Part II deals with the integration of spatial data sources; Part III describes approaches to spatial data protection; and, finally, Part IV reports innovative applications for mobile devices. The book offers researchers in academia and industry an excellent overview of the state of the art in modeling and management of spatial data in distributed environments, while it may also be the basis of specialized courses on Web-based geographical information systems.

Vast amounts of data are collected by service providers and system administ- tors, and are available in public information systems. Data mining technologies provide an ideal framework to assist in analyzing such collections for computer security and surveillance-related endeavors. For instance, system administrators can apply data mining to summarize activity patterns in access logs so that potential malicious incidents can be further investigated. Beyond computer - curity, data mining technology supports intelligence gathering and summari- tion for homeland security. For years, and most recently fueled by events such as September 11, 2001, government agencies have focused on developing and applying data mining technologies to monitor terrorist behaviors in public and private data collections. Theapplicationof data mining to person-speci?cdata raisesseriousconcerns regarding data con?dentiality and citizens' privacy rights. These concerns have led to the adoption of various legislation and policy controls.In 2005, the - ropean Union passed a data-retention directive that requires all telephone and Internetservice providersto store data ontheir consumers for up to two yearsto assist in the prevention of terrorismand organized crime. Similar data-retention regulationproposalsareunderheateddebateintheUnitedStatesCongress. Yet, the debate often focuses on ethical or policy aspects of the problem, such that resolutions have polarized consequences; e. g. , an organization can either share data for data mining purposes or it can not. Fortunately, computer scientists, and data mining researchers in particular, have recognized that technology can beconstructedtosupportlesspolarizedsolutions. Computerscientistsaredev- oping technologies that enable data mining goals without sacri?cing the privacy and security of the individuals to whom the data correspond.

This book constitutes the thoroughly refereed post-workshop proceedings of the Second International Workshop on Privacy, Security, and Trust in KDD, PinKDD 2008, held in Las Vegas, NV, USA, in March 2008 in conjunction with the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008. The 5 revised full papers presented together with 1 invited keynote lecture and 2 invited panel sessions were carefully reviewed and selected from numerous submissions. The papers are extended versions of the workshop presentations and incorporate reviewers' comments and discussions at the workshop and represent the diversity of data mining research issues in privacy, security, and trust as well as current work on privacy issues in geographic data mining.

This book constitutes the refereed proceedings of the Third IFIP WG 11.11 International Conference, IFIPTM 2009, held in West Lafayette, IN, USA, in June 2009. The 17 revised full papers presented together with one invited paper and 5 demo descriptions were carefully reviewed and selected from 44 submissions. The papers are organized in topical sections on social aspects and usability, trust reasoning and processing, data security, enhancements to subjective logic, information sharing, risk assessment, and simulation of trust and reputation systems.

Spatial data is essential in a wide range of application domains today. While geographical applications remain the key target area, spatial properties are required in other contexts such as computer-aided design, robotics and image processing. Associated with these is the constantly growing number of distributed processing architectures, based on, for example, grid systems, sensor data networks, and personalized smart devices.Spatial Data on the Web links these two research streams, focusing on the modeling and management of spatial data in distributed systems. Belussi and his coeditors have structured the contributions from internationally renowned researchers into four parts. Part I presents models for representing semistructured, multiresolution and multiscale data; Part II deals with the integration of spatial data sources; Part III describes approaches to spatial data protection; and, finally, Part IV reports innovative applications for mobile devices. The book offers researchers in academia and industry an excellent overview of the state of the art in modeling and management of spatial data in distributed environments, while it may also be the basis of specialized courses on Web-based geographical information systems.