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.
Rezensionen / Stimmen
From the reviews:
"This book provides an exceptional summary of the state-of-the-art accomplishments in the area of privacy-preserving data mining, discussing the most important algorithms, models, and applications in each direction. The target audience includes researchers, graduate students, and practitioners who are interested in this area. . I recommend this book to all readers interested in privacy-preserving data mining." (Aris Gkoulalas-Divanis, ACM Computing Reviews, October, 2008)
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ISBN-13
978-0-387-70992-5 (9780387709925)
DOI
10.1007/978-0-387-70992-5
Schweitzer Klassifikation
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.
