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Privacy-Preserving Data Mining

Models and Algorithms

By Charu C. Aggarwal , Philip S. Yu

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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

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