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Privacy-Preserving Data Mining

Models and Algorithms

By Charu C. Aggarwal , Philip S. Yu

  • eBook Price: $159.00 $95.40
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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.

Full Description

  • ISBN13: 978-0-3877-0991-8
  • 536 Pages
  • User Level: Science
  • Publication Date: June 10, 2008
  • Available eBook Formats: PDF

Related Titles

Full Description
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.
Table of Contents

Table of Contents

  1. An Introduction to Privacy
  2. Preserving Data Mining.
  3. A General Survey of Privacy
  4. Preserving Data Mining Models and Algorithms.
  5. A Survey of Inference Control Methods for Privacy
  6. Preserving Data Mining.
  7. Measures of Anonymity.
  8. k
  9. Anonymous Data Mining: A Survey.
  10. A Survey of Randomization Methods for Privacy
  11. Preserving Data Mining.
  12. A Survey of Multiplicative Perturbation for Privacy
  13. Preserving Data Mining.
  14. A Survey of Quantification of Privacy Preserving Data Mining Algorithms.
  15. A Survey of Utility
  16. based Privacy
  17. Preserving Data Transformation Methods.
  18. Mining Association Rules under Privacy Constraints.
  19. A Survey of Association Rule Hiding Methods for Privacy.
  20. A Survey of Statistical Approaches to Preserving Confidentiality of Contingency Table Entries.
  21. A Survey of Privacy
  22. Preserving Methods Across Horizontally Partitioned Data.
  23. Survey of Privacy
  24. Preserving Methods across Vertically Partitioned Data.
  25. A Survey of Attack Techniques on Privacy
  26. Preserving Data Perturbation Methods.
  27. Private Data Analysis via Output Perturbation.
  28. A Survey of Query Auditing Techniques for Data Privacy.
  29. Privacy and the Dimensionality Curse.
  30. Personalized Privacy Preservation .
  31. Privacy
  32. Preserving Data Stream Classification.
  33. Index.

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