TY  - BOOK
AU  - Aggarwal,Charu C.
AU  - Yu,Philip S.
ED  - SpringerLink (Online service)
TI  - Privacy-Preserving Data Mining: Models and Algorithms 
T2  - Advances in Database Systems,
SN  - 9780387709925
U1  - 005.8 23
PY  - 2008///
CY  - Boston, MA
PB  - Springer US
KW  - COMPUTER SCIENCE
KW  - DATA PROTECTION
KW  - DATA ENCRYPTION (COMPUTER SCIENCE)
KW  - DATABASE MANAGEMENT
KW  - DATA MINING
KW  - INFORMATION STORAGE AND RETRIEVAL SYSTEMS
KW  - INFORMATION SYSTEMS
KW  - SYSTEMS AND DATA SECURITY
KW  - DATA MINING AND KNOWLEDGE DISCOVERY
KW  - DATA ENCRYPTION
KW  - INFORMATION STORAGE AND RETRIEVAL
KW  - INFORMATION SYSTEMS APPLICATIONS (INCL.INTERNET)
N1  - An Introduction to Privacy-Preserving Data Mining -- A General Survey of Privacy-Preserving Data Mining Models and Algorithms -- A Survey of Inference Control Methods for Privacy-Preserving Data Mining -- Measures of Anonymity -- k-Anonymous Data Mining: A Survey -- A Survey of Randomization Methods for Privacy-Preserving Data Mining -- A Survey of Multiplicative Perturbation for Privacy-Preserving Data Mining -- A Survey of Quantification of Privacy Preserving Data Mining Algorithms -- A Survey of Utility-based Privacy-Preserving Data Transformation Methods -- Mining Association Rules under Privacy Constraints -- A Survey of Association Rule Hiding Methods for Privacy -- A Survey of Statistical Approaches to Preserving Confidentiality of Contingency Table Entries -- A Survey of Privacy-Preserving Methods Across Horizontally Partitioned Data -- A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data -- A Survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods -- Private Data Analysis via Output Perturbation -- A Survey of Query Auditing Techniques for Data Privacy -- Privacy and the Dimensionality Curse -- Personalized Privacy Preservation -- Privacy-Preserving Data Stream Classification
N2  - Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals. This has caused concerns that personal data may be used for a variety of intrusive or malicious purposes. Privacy Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. These techniques generally fall into the following categories: data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques. This edited volume also contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy. Privacy Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science. This book is also suitable for practitioners in industry.
UR  - http://dx.doi.org/10.1007/978-0-387-70992-5
ER  -