Privacy-Preserving Data Mining : Models and Algorithms

Philip S. Yu

ISBN 10: 1441943714 / ISBN 13: 9781441943712

Editore: Springer US, Springer US, 2010

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Druck auf Anfrage Neuware - Printed after ordering - Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals, causing 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 contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions.Privacy-Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science, and is also suitable for industry practitioners. Codice articolo 9781441943712

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Titolo: Privacy-Preserving Data Mining : Models and ...

Casa editrice: Springer US, Springer US

Data di pubblicazione: 2010

Legatura: Taschenbuch

Condizione: Neu

Descrizione articolo

Riassunto:

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.

Recensione:

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)