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Advances in Database Systems

Privacy-Preserving Data Mining

Models and Algorithms

Editors: Aggarwal, Charu C., Yu, Philip S. (Eds.)

  • Occupies an important niche in the privacy-preserving data mining field
  • Survey information included with each chapter is unique in terms of its focus on introducing the different topics more comprehensively
  • Provides relative understanding of the work of different communities, such as cryptography, statistical disclosure control, data mining working in the privacy field
  • Comprehensive and current, bringing together different points of view
  • Key advances in privacy that just appeared in past three years
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eBook $179.00
price for USA
  • ISBN 978-0-387-70992-5
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $229.00
price for USA
  • ISBN 978-0-387-70991-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $229.00
price for USA
  • ISBN 978-1-4419-4371-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.

 

Reviews

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)


Table of contents (20 chapters)

  • An Introduction to Privacy-Preserving Data Mining

    Aggarwal, Charu C. (et al.)

    Pages 1-9

  • A General Survey of Privacy-Preserving Data Mining Models and Algorithms

    Aggarwal, Charu C. (et al.)

    Pages 11-52

  • A Survey of Inference Control Methods for Privacy-Preserving Data Mining

    Domingo-Ferrer, Josep

    Pages 53-80

  • Measures of Anonymity

    Venkatasubramanian, Suresh

    Pages 81-103

  • k

    Ciriani, V. (et al.)

    Pages 105-136

Buy this book

eBook $179.00
price for USA
  • ISBN 978-0-387-70992-5
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $229.00
price for USA
  • ISBN 978-0-387-70991-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $229.00
price for USA
  • ISBN 978-1-4419-4371-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Privacy-Preserving Data Mining
Book Subtitle
Models and Algorithms
Editors
  • Charu C. Aggarwal
  • Philip S. Yu
Series Title
Advances in Database Systems
Series Volume
34
Copyright
2008
Publisher
Springer US
Copyright Holder
Springer-Verlag US
eBook ISBN
978-0-387-70992-5
DOI
10.1007/978-0-387-70992-5
Hardcover ISBN
978-0-387-70991-8
Softcover ISBN
978-1-4419-4371-2
Series ISSN
1386-2944
Edition Number
1
Number of Pages
XXII, 514
Topics