Privacy-Preserving Data Mining Models and Algorithms

Privacy-Preserving Data Mining Models and Algorithms / edited by Charu C. Aggarwal, Philip S. Yu.

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...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Aggarwal, Charu C. (Editor), Yu, Philip S. (Editor)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 2008.
Edition:1st ed. 2008.
Series:Advances in Database Systems,
Springer eBook Collection.
Subjects:
Computer security.
Data mining.
Data encryption (Computer science).
Database management.
Information storage and retrieval.
Application software.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • An Introduction to Privacy-Preserving Data Mining
  • A General Survey of Privacy-Preserving Data Mining Models and Algorithms
  • A Survey of Inference Control Methods for Privacy-Preserving Data Mining
  • Measures of Anonymity
  • k-Anonymous Data Mining: A Survey
  • A Survey of Randomization Methods for Privacy-Preserving Data Mining
  • A Survey of Multiplicative Perturbation for Privacy-Preserving Data Mining
  • A Survey of Quantification of Privacy Preserving Data Mining Algorithms
  • A Survey of Utility-based Privacy-Preserving Data Transformation Methods
  • Mining Association Rules under Privacy Constraints
  • A Survey of Association Rule Hiding Methods for Privacy
  • A Survey of Statistical Approaches to Preserving Confidentiality of Contingency Table Entries
  • A Survey of Privacy-Preserving Methods Across Horizontally Partitioned Data
  • A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data
  • A Survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods
  • Private Data Analysis via Output Perturbation
  • A Survey of Query Auditing Techniques for Data Privacy
  • Privacy and the Dimensionality Curse
  • Personalized Privacy Preservation
  • Privacy-Preserving Data Stream Classification.

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