Privacy-Preserving Data Mining: Models and Algorithms

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