Privacy-Preserving Data Mining

Models and Algorithms

Inbunden, Engelska, 2008

AvCharu C. Aggarwal,Philip S. Yu,Charu C Aggarwal,Philip S Yu

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This book proposes a number of techniques to perform data mining tasks in a privacy-preserving way. The survey information included with each chapter is unique in terms of its focus on introducing the different topics more comprehensively.

Produktinformation

Utgivningsdatum2008-07-07
Mått155 x 235 x 31 mm
Vikt860 g
FormatInbunden
SpråkEngelska
SerieAdvances in Database Systems
Antal sidor514
Upplaga2008
FörlagSpringer-Verlag New York Inc.
ISBN9780387709918

Tillhör följande kategorier

Tillämpad datateknik inom Data och it
Systemvetenskap och AI inom Data och it
Nätverk och kommunikation inom Data och it
Referensverk och tvärvetenskap inom Samhalle och politik
Databaser inom Data och it
IT-säkerhet inom Data och it

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.

From the reviews: "This book provides an exceptional summary of the state-of-the-art accomplishments in the area of privacy-preserving data mining, discussing the most important algorithms, models, and applications in each direction. The target audience includes researchers, graduate students, and practitioners who are interested in this area. ... I recommend this book to all readers interested in privacy-preserving data mining." (Aris Gkoulalas-Divanis, ACM Computing Reviews, October, 2008)