Privacy Preserving Data Mining

Jaideep Vaidya|Christopher W. Clifton|Yu Michael Zhu

ISBN 10: 1441938478 / ISBN 13: 9781441938473

Published by Springer US, 2010

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First book on privacy preserving data mining - a real application of secure computationWritten for researchers who wish to enter the field and need to know the state of the art methods for developing algorithms, and how to prove privacyAl.

Seller Inventory # 4174219

Bibliographic Details

• Title: Privacy Preserving Data Mining
• Publisher: Springer US
• Publication Date: 2010
• Language: English
• ISBN 10: 1441938478
• ISBN 13: 9781441938473
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area.

Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.

From the Back Cover

Data mining has emerged as a significant technology for gaining knowledge from vast quantities of data. However, concerns are growing that use of this technology can violate individual privacy. These concerns have led to a backlash against the technology, for example, a "Data-Mining Moratorium Act" introduced in the U.S. Senate that would have banned all data-mining programs (including research and development) by the U.S. Department of Defense.

Privacy Preserving Data Mining provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. This book demonstrates how these approaches can achieve data mining, while operating within legal and commercial restrictions that forbid release of data. Furthermore, this research crystallizes much of the underlying foundation, and inspires further research in the area.

Privacy Preserving Data Mining is designed for a professional audience composed of practitioners and researchers in industry. This volume is also suitable for graduate-level students in computer science.