Privacy Preserving Data Mining

Jaideep Vaidya, Christopher W. Clifton, Yu Michael Zhu - Collection Advances in Information Security

130 pages, parution le 09/01/2006

Ajouter à une liste

Indisponible

Résumé

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.

Written for: Researchers, industry practitioners, policy makers

Sommaire

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

Voir tout

Replier

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Jaideep Vaidya, Christopher W. Clifton, Yu Michael Zhu
Collection	Advances in Information Security
Parution	09/01/2006
Nb. de pages	130
Format	16 x 24
Couverture	Broché
Poids	350g
Intérieur	Noir et Blanc
EAN13	9780387258867
ISBN13	978-0-387-25886-7