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.

First book on privacy preserving data mining - a real application of secure computation Written 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" privacy Also intended for practitioners who need advice on privacy-preserving data mining applications, how to apply it, and what to watch out for Includes supplementary material: sn.pub/extras

Inhalt
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.