Jaideep Vaidya, Christopher W. Clifton, Yu Michael Zhu

Privacy Preserving Data Mining (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 122
• Erscheinungstermin: 28. September 2006
• Englisch
• ISBN-13: 9780387294896
• Artikelnr.: 37288218

Autorenporträt

Jaideep Vaidya, Rutgers University, Newark, NJ, USA / Yu Michael Zhu, Purdue, W. Lafayette, IN, USA / Christopher W. Clifton, Purdue, W. Lafayette, IN, USA

Inhaltsangabe

Privacy and Data Mining.- What is Privacy?.- Solution Approaches / Problems.- Predictive Modeling for Classification.- Predictive Modeling for Regression.- Finding Patterns and Rules (Association Rules).- Descriptive Modeling (Clustering, Outlier Detection).- Future Research - Problems remaining.