Privacy-Preserving Data Mining

Aggarwal, Charu C.|Yu, Philip S.

ISBN 10: 1441943714 / ISBN 13: 9781441943712

Published by Springer US, 2010

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Occupies an important niche in the privacy-preserving data mining fieldSurvey information included with each chapter is unique in terms of its focus on introducing the different topics more comprehensivelyProvides relative und.

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Bibliographic Details

• Title: Privacy-Preserving Data Mining
• Publisher: Springer US
• Publication Date: 2010
• Language: English
• ISBN 10: 1441943714
• ISBN 13: 9781441943712
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Advances 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.

 

From the Back Cover

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.