Privacy-Preserving Data Mining
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.
| ISBN: | 9781441943712 |
|---|---|
| Sprache: | Englisch |
| Seitenzahl: | 514 |
| Produktart: | Kartoniert / Broschiert |
| Herausgeber: | Aggarwal, Charu C. Yu, Philip S. |
| Verlag: | Springer US |
| Veröffentlicht: | 19.11.2010 |
| Untertitel: | Models and Algorithms |
| Schlagworte: | DOM Information K-anonymity algorithms association rule hiding classification cryptographic approaches data analysis data mining distributed priv |