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 contains surveys by distinguished researchers in the privacy field. Each survey includes the keyresearch content as well as future research directions of a particular topic in privacy. Occupies an important niche in the privacy-preserving data mining field Survey information included with each chapter is unique in terms of its focus on introducing the different topics more comprehensively Provides relative understanding of the work of different communities, such as cryptography, statistical disclosure control, data mining working in the privacy field Key advances in privacy INDICE: From the 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-PreservingMethods Across Horizontally Partitioned Data.- Survey of Privacy-Preserving Methods across Vertically Partitioned Data.
- ISBN: 978-0-387-70991-8
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 535
- Fecha Publicación: 01/03/2008
- Nº Volúmenes: 1
- Idioma: Inglés