TY  - GEN
TY  - GEN
T1  - Privacy-Preserving Data Mining Models and Algorithms
T2  - Advances in Database Systems,
A2  - Aggarwal, Charu C.
A2  - Aggarwal, Charu C.
A2  - Yu, Philip S.
A2  - Yu, Philip S.
LA  - English
PP  - New York, NY
PB  - Springer US : Imprint: Springer
YR  - 2008
ED  - 1st ed. 2008.
UL  - http://discoverylib.upm.edu.my/discovery/Record/978-0-387-70992-5
AB  - 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. .
OP  - 514
CN  - QA76.9.A25
SN  - 9780387709925
KW  - Computer security.
KW  - Data mining.
KW  - Data encryption (Computer science).
KW  - Database management.
KW  - Information storage and retrieval.
KW  - Application software.
KW  - Systems and Data Security.
KW  - Data Mining and Knowledge Discovery.
KW  - Cryptology.
KW  - Database Management.
KW  - Information Storage and Retrieval.
KW  - Information Systems Applications (incl. Internet).
ER  -