© 2008

Privacy-Preserving Data Mining

Models and Algorithms

  • Charu C. Aggarwal
  • Philip S. Yu
  • 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


Part of the Advances in Database Systems book series (ADBS, volume 34)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Charu C. Aggarwal, Philip S. Yu
    Pages 1-9
  3. Suresh Venkatasubramanian
    Pages 81-103
  4. V. Ciriani, S. De Capitani di Vimercati, S. Foresti, P. Samarati
    Pages 105-136
  5. Charu C. Aggarwal III, Philip S. Yu
    Pages 137-156
  6. Elisa Bertino, Dan Lin, Wei Jiang
    Pages 183-205
  7. Jayant R. Haritsa
    Pages 239-266
  8. Vassilios S. Verykios, Aris Gkoulalas-Divanis
    Pages 267-289
  9. Kun Liu, Chris Giannella, Hillol Kargupta
    Pages 359-381
  10. Shubha U. Nabar, Krishnaram Kenthapadi, Nina Mishra, Rajeev Motwani
    Pages 415-431
  11. Charu C. Aggarwal
    Pages 433-460
  12. Yufei Tao, Xiaokui Xiao
    Pages 461-485

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.



DOM Information K-anonymity algorithms association rule hiding classification cryptographic approaches data analysis data mining distributed priv personalized privacy privacy query auditing randonization stream privacy

Editors and affiliations

  • Charu C. Aggarwal
    • 1
  • Philip S. Yu
    • 2
  1. 1.IBM Thomas J. Watson Research CenterHawthorneUSA
  2. 2.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

Bibliographic information

Industry Sectors
Chemical Manufacturing
IT & Software
Consumer Packaged Goods
Finance, Business & Banking
Energy, Utilities & Environment


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)