Secure Data Mining - Zhan, Justin; Matwin, Stan; - Prospero Internet Bookshop

 
Product details:

ISBN13:9780387879659
ISBN10:038787965X
Binding:Hardback
No. of pages:280 pages
Size:235x155 mm
Language:English
Illustrations: 20 Illustrations, black & white
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Category:

Secure Data Mining

 
Edition number: 1st ed. 2024
Publisher: Springer
Date of Publication:
Number of Volumes: 1 pieces, Book
 
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EUR 71.64
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Short description:

This book provides solutions to the problem of data mining without compromising data privacy. This professional book is designed for practitioners and researchers in industry, as well as a secondary textbook for advanced-level students in computer science.

Long description:

Data mining is a process to extract useful knowledge from large amounts of data. To conduct data mining, we often need to collect data. However, privacy concerns may prevent people from sharing the data and some types of information about the data. How we conduct data mining without breaching data privacy presents a challenge.


Secure Data Mining provides solutions to the problem of data mining without compromising data privacy. This professional book is designed for practitioners and researchers in industry, as well as a secondary textbook for advanced-level students in computer science.

Table of Contents:

Preface.- Introduction.- Literature Review.- Fundamental Security and Privacy.- Privacy-Preserving Association Rule Mining.- Privacy-Preserving Sequential Pattern Mining.- Privacy-Preserving Naive Bayesian Classification.- Privacy-Preserving Decision Tree Classification.- Privacy-Preserving k-Nearest Neighbor Classification.- Privacy-Preserving Support Vector Machine Classification.- Privacy-Preserving k-Mean Clustering.- Privacy-Preserving k-Medoids Clustering.- Other Selected Topics.- Conclusion and Future Work.- Index.