Charu C. Aggarwal,
Philip S. Yu
Springer US
2008e druk, 2008
9780387709918
Privacy-Preserving Data Mining
Models and Algorithms
Specificaties
Gebonden, 514 blz.
|
Engels
Springer US |
2008e druk, 2008
ISBN13: 9780387709918
Rubricering
Onderdeel van serie
Advances in Database Systems
Levertijd ongeveer 9 werkdagen
Gratis verzonden
Samenvatting
Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume 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. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.
Specificaties
ISBN13:9780387709918
Taal:Engels
Bindwijze:gebonden
Aantal pagina's:514
Uitgever:Springer US
Druk:2008
Hoofdrubriek:Databases, Computer en informatica
Inhoudsopgave
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.