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 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 also 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.
Privacy Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science. This book is also suitable for practitioners in industry.
Author(s): Charu C. Aggarwal, Philip S. Yu (auth.), Charu C. Aggarwal, Philip S. Yu (eds.)
Series: Advances in Database Systems 34
Edition: 1
Publisher: Springer US
Year: 2008
Language: English
Pages: 514
Tags: Systems and Data Security; Data Mining and Knowledge Discovery; Database Management; Data Encryption; Information Storage and Retrieval; Information Systems Applications (incl.Internet)
Front Matter....Pages i-xxii
An Introduction to Privacy-Preserving Data Mining....Pages 1-9
A General Survey of Privacy-Preserving Data Mining Models and Algorithms....Pages 11-52
A Survey of Inference Control Methods for Privacy-Preserving Data Mining....Pages 53-80
Measures of Anonymity....Pages 81-103
k -Anonymous Data Mining: A Survey....Pages 105-136
A Survey of Randomization Methods for Privacy-Preserving Data Mining....Pages 137-156
A Survey of Multiplicative Perturbation for Privacy-Preserving Data Mining....Pages 157-181
A Survey of Quantification of Privacy Preserving Data Mining Algorithms....Pages 183-205
A Survey of Utility-based Privacy-Preserving Data Transformation Methods....Pages 207-237
Mining Association Rules under Privacy Constraints....Pages 239-266
A Survey of Association Rule Hiding Methods for Privacy....Pages 267-289
A Survey of Statistical Approaches to Preserving Confidentiality of Contingency Table Entries....Pages 291-312
A Survey of Privacy-Preserving Methods Across Horizontally Partitioned Data....Pages 313-335
A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data....Pages 337-358
A Survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods....Pages 359-381
Private Data Analysis via Output Perturbation....Pages 383-414
A Survey of Query Auditing Techniques for Data Privacy....Pages 415-431
Privacy and the Dimensionality Curse....Pages 433-460
Personalized Privacy Preservation....Pages 461-485
Privacy-Preserving Data Stream Classification....Pages 487-510
Back Matter....Pages 511-513
