PRIVACY PRESERVING DATA MINING (HB)
Language: English
Published by Springer, 2005
- Hardcover
- New
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- Title
- PRIVACY PRESERVING DATA MINING (HB)
- Author
- Vaidya, Jaideep; Clifton, Christopher W.; Zhu, Yu Michael
- Publisher
- Springer
- Publication year
- 2005
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0387258868
- ISBN 13
- 9780387258867
- Series
- Book 19 of 109: Advances in Information Security
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
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