Privacy-Preserving Data Mining: Models and Algorithms (Advances in Database Systems)

Aggarwal, Charu C. (Editor) / Yu, Philip S. (Editor)

ISBN 10: 1441943714 / ISBN 13: 9781441943712

Editore: Springer, 2008

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513 pages. 9.30x6.10x1.20 inches. In Stock.

Codice articolo x-1441943714

Dati bibliografici

• Titolo: Privacy-Preserving Data Mining: Models and Algorithms (Advances in Database Systems)
• Editore: Springer
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 1441943714
• ISBN 13: 9781441943712
• Rilegatura: Paperback
• Condizione: Brand New

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Riassunto

Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals. This has caused concerns that personal data may be used for a variety of intrusive or malicious purposes. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume contains surveys by distinguished researchers in the privacy field. The survey information included with each chapter is unique in terms of its focus on introducing the different topics more comprehensively. Key advances in privacy that have appeared only in the past three years are covered. The book is designed for researchers, professors, and advanced-level students in computer science. It is also suitable for practitioners in industry.

Dalla quarta di copertina

Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals. This has caused 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 also contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy.

Privacy Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science. This book is also suitable for practitioners in industry.