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

Mobile communications and ubiquitous computing generate large volumes of data. Mining this data can produce useful knowledge, yet individual privacy is at risk. This book investigates the various scientific and technological issues of mobility data, open problems, and roadmap. The editors manage a research project called GeoPKDD, Geographic Privacy-Aware Knowledge Discovery and Delivery, and this book relates their findings in 13 chapters covering all related subjects.

This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.

Knowledge discovery in ubiquitous environments is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems. It aims to provide a unifying framework for systematically investigating the mutual dependencies of otherwise quite unrelated technologies employed in building next-generation intelligent systems: machine learning, data mining, sensor networks, grids, peer-to-peer networks, data stream mining, activity recognition, Web 2.0, privacy, user modelling and others. This state-of-the-art survey is the outcome of a large number of workshops, summer schools, tutorials and dissemination events organized by KDubiq (Knowledge Discovery in Ubiquitous Environments), a networking project funded by the European Commission to bring together researchers and practitioners of this emerging community. It provides in its first part a conceptual foundation for the new field of ubiquitous knowledge discovery - highlighting challenges and problems, and proposing future directions in the area of 'smart', 'adaptive', and 'intelligent' learning. The second part of this volume contains selected approaches to ubiquitous knowledge discovery and treats specific aspects in detail. The contributions have been carefully selected to provide illustrations and in-depth discussions for some of the major findings of Part I.

Knowledge discovery in ubiquitous environments is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems. It aims to provide a unifying framework for systematically investigating the mutual dependencies of otherwise quite unrelated technologies employed in building next-generation intelligent systems: machine learning, data mining, sensor networks, grids, peer-to-peer networks, data stream mining, activity recognition, Web 2.0, privacy, user modelling and others. This state-of-the-art survey is the outcome of a large number of workshops, summer schools, tutorials and dissemination events organized by KDubiq (Knowledge Discovery in Ubiquitous Environments), a networking project funded by the European Commission to bring together researchers and practitioners of this emerging community. It provides in its first part a conceptual foundation for the new field of ubiquitous knowledge discovery - highlighting challenges and problems, and proposing future directions in the area of 'smart', 'adaptive', and 'intelligent' learning. The second part of this volume contains selected approaches to ubiquitous knowledge discovery and treats specific aspects in detail. The contributions have been carefully selected to provide illustrations and in-depth discussions for some of the major findings of Part I.

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 11th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2009 held in Linz, Austria in August/September 2009. The 36 revised full papers presented were carefully reviewed and selected from 124 submissions. The papers are organized in topical sections on data warehouse modeling, data streams, physical design, pattern mining, data cubes, data mining applications, analytics, data mining, clustering, spatio-temporal mining, rule mining, and OLAP recommendation.

On 25 January 2012, the European Commission presented its long awaited new “Data protection package”. With this proposal for a drastic revision of the data protection framework in Europe, it is fair to say that we are witnessing a rebirth of European data protection, and perhaps, its passage from an impulsive youth to a more mature state. Technology advances rapidly and mobile devices are significantly changing the landscape. Increasingly, we carry powerful, connected, devices, whose location and activities can be monitored by various stakeholders. Very powerful social network sites emerged in the first half of last decade, processing personal data of many millions of users. Updating the regulatory network was imminent and the presentation of the new package will initiate a period of intense debate in which the proposals will be thoroughly commented upon and criticized, and numerous amendments will undoubtedly be proposed. This volume brings together some 19 chapters offering conceptual analyses, highlighting issues, proposing solutions, and discussing practices regarding privacy and data protection. In the first part of the book, conceptual analyses of concepts such as privacy and anonymity are provided. The second section focuses on the contrasted positions of digital natives and ageing users in the information society. The third section provides four chapters on privacy by design, including discussions on roadmapping and concrete techniques. The fourth section is devoted to surveillance and profiling, with illustrations from the domain of smart metering, self-surveillance and the benefits and risks of profiling. The book concludes with case studies pertaining to communicating privacy in organisations, the fate of a data protection supervisor in one of the EU member states and data protection in social network sites and online media. This volume brings together some 19 chapters offering conceptual analyses, highlighting issues, proposing solutions, and discussing practices regarding privacy and data protection. In the first part of the book, conceptual analyses of concepts such as privacy and anonymity are provided. The second section focuses on the contrasted positions of digital natives and ageing users in the information society. The third section provides four chapters on privacy by design, including discussions on roadmapping and concrete techniques. The fourth section is devoted to surveillance and profiling, with illustrations from the domain of smart metering, self-surveillance and the benefits and risks of profiling. The book concludes with case studies pertaining to communicating privacy in organisations, the fate of a data protection supervisor in one of the EU member states and data protection in social network sites and online media.

This timely interdisciplinary work on current developments in ICT and privacy/data protection, coincides as it does with the rethinking of the Data Protection Directive, the contentious debates on data sharing with the USA (SWIFT, PNR) and the judicial and political resistance against data retention. The authors of the contributions focus on particular and pertinent issues from the perspective of their different disciplines which range from the legal through sociology, surveillance studies and technology assessment, to computer sciences. Such issues include cutting-edge developments in the field of cloud computing, ambient intelligence and PETs; data retention, PNR-agreements, property in personal data and the right to personal identity; electronic road tolling, HIV-related information, criminal records and teenager's online conduct, to name but a few.

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns. Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations. Provides an introduction to KDDM, including the various models adopted in academia and industry Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility Demonstrates the applicability of the KDDM process beyond analytics Shares experiences of implementing and applying various stages of the KDDM process in organizations The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization’s strategic business objectives.