Privacy-Preserving Data Mining: Models and Algorithms Front Cover

Privacy-Preserving Data Mining: Models and Algorithms

  • Length: 535 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2008-07-07
  • ISBN-10: 0387709916
  • ISBN-13: 9780387709918
  • Sales Rank: #1688021 (See Top 100 Books)
Description

Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. 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 of a particular topic in privacy. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.

Table of Contents

1 An Introduction to Privacy-Preserving Data Mining
2 A General Survey of Privacy-Preserving Data Mining Models and Algorithms
3 A Survey of Inference Control Methods for Privacy-Preserving Data Mining
4 Measures of Anonymity Suresh Venkatasubramanian
5 k-Anonymous Data Mining: A Survey
6 A Survey of RandomizationMethods for Privacy-PreservingDataMining
7 A Survey of Multiplicative Perturbation for Privacy-Preserving Data Mining
8 ASurvey ofQuantification of Privacy PreservingDataMiningAlgorithms
9 A Survey of Utility-based Privacy-Preserving Data Transformation Methods
10 Mining Association Rules under Privacy Constraints
11 A Survey of Association Rule Hiding Methods for Privacy
12 A Survey of Statistical Approaches to Preserving Confidentiality of Contingency Table Entries
13 A Survey of Privacy-Preserving Methods Across Horizontally Partitioned Data
14 A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data
15 A Survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods
16 Private Data Analysis via Output Perturbation
17 A Survey of Query Auditing Techniques for Data Privacy
18 Privacy and the Dimensionality Curse
19 Personalized Privacy Preservation
20 Privacy-Preserving Data Stream Classification

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