Editors:
- 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
- Key advances in privacy
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Database Systems (ADBS)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (20 chapters)
-
Front Matter
About this book
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.
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)
Editors and Affiliations
-
IBM Thomas J. Watson Research Center, Hawthorne, USA
Charu C. Aggarwal
-
Department of Computer Science, University of Illinois at Chicago, Chicago, USA
Philip S. Yu
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
DOI: https://doi.org/10.1007/978-0-387-70992-5
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag US 2008
Hardcover ISBN: 978-0-387-70991-8Published: 07 July 2008
Softcover ISBN: 978-1-4419-4371-2Published: 19 November 2010
eBook ISBN: 978-0-387-70992-5Published: 10 June 2008
Series ISSN: 1386-2944
Edition Number: 1
Number of Pages: XXII, 514
Topics: Systems and Data Security, Data Mining and Knowledge Discovery, Cryptology, Database Management, Information Storage and Retrieval, Information Systems Applications (incl. Internet)
Industry Sectors: Aerospace, Automotive, Biotechnology, Electronics, Engineering, Finance, Business & Banking, IT & Software, Law, Pharma, Telecommunications