Privacy-Preserving Data Mining (eBook, PDF)

Models and Algorithms
Redaktion: Aggarwal, Charu C.; Yu, Philip S.

• Format: PDF

Produktbeschreibung

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. 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. The survey information included with each chapter is unique in terms of its focus on introducing the different topics more comprehensively. Key advances in privacy that have appeared only in the past three years are covered. The book is designed for researchers, professors, and advanced-level students in computer science. It is also suitable for practitioners in industry.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 514
• Erscheinungstermin: 10. Juni 2008
• Englisch
• ISBN-13: 9780387709925
• Artikelnr.: 37288445

Inhaltsangabe

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.

Rezensionen

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)