TY  - BOOK
AU  - Vaidya,Jaideep
AU  - Zhu,Yu Michael
AU  - Clifton,Christopher W.
ED  - SpringerLink (Online service)
TI  - Privacy Preserving Data Mining
T2  - Advances in Information Security,
SN  - 9780387294896
AV  - QA76.9.D343 
U1  - 006.312 23
PY  - 2006///
CY  - Boston, MA
PB  - Springer US
KW  - Computer science
KW  - Computer Communication Networks
KW  - Data structures (Computer science)
KW  - Data encryption (Computer science)
KW  - Database management
KW  - Data mining
KW  - Information storage and retrieval systems
KW  - Computer Science
KW  - Data Mining and Knowledge Discovery
KW  - Database Management
KW  - Data Structures, Cryptology and Information Theory
KW  - Data Encryption
KW  - Information Storage and Retrieval
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-0-387-29489-6
ER  -