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OverviewAdvances in hardware technology have increased the capability to store and record personal data about consumers and individuals, causing concerns that personal data may be used for a variety of intrusive or malicious purposes. Privacy-Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. These techniques generally fall into the following categories: data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions. Privacy-Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science, and is also suitable for industry practitioners. Full Product DetailsAuthor: Charu C. Aggarwal , Philip S. YuPublisher: Springer-Verlag New York Inc. Imprint: Springer-Verlag New York Inc. Edition: Softcover reprint of hardcover 1st ed. 2008 Volume: 34 Dimensions: Width: 15.50cm , Height: 2.70cm , Length: 23.50cm Weight: 0.813kg ISBN: 9781441943712ISBN 10: 1441943714 Pages: 514 Publication Date: 19 November 2010 Audience: Professional and scholarly , Professional & Vocational , Postgraduate, Research & Scholarly Format: Paperback Publisher's Status: Active Availability: Out of print, replaced by POD  We will order this item for you from a manufatured on demand supplier. Table of ContentsReviewsFrom the reviews: This book provides an exceptional summary of the state-of-the-art accomplishments in the area of privacy-preserving data mining, discussing the most important algorithms, models, and applications in each direction. The target audience includes researchers, graduate students, and practitioners who are interested in this area. ... I recommend this book to all readers interested in privacy-preserving data mining. (Aris Gkoulalas-Divanis, ACM Computing Reviews, October, 2008) From the reviews: This book provides an exceptional summary of the state-of-the-art accomplishments in the area of privacy-preserving data mining, discussing the most important algorithms, models, and applications in each direction. The target audience includes researchers, graduate students, and practitioners who are interested in this area. ... I recommend this book to all readers interested in privacy-preserving data mining. (Aris Gkoulalas-Divanis, ACM Computing Reviews, October, 2008) From the reviews: This book provides an exceptional summary of the state-of-the-art accomplishments in the area of privacy-preserving data mining, discussing the most important algorithms, models, and applications in each direction. The target audience includes researchers, graduate students, and practitioners who are interested in this area. ! I recommend this book to all readers interested in privacy-preserving data mining. (Aris Gkoulalas-Divanis, ACM Computing Reviews, October, 2008) Author InformationTab Content 6Author Website:Countries AvailableAll regions |
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