Embedding Privacy in Data Mining

Arik Friedman

ISBN 10: 3847303635 / ISBN 13: 9783847303633

Published by LAP LAMBERT Academic Publishing Dez 2011, 2011

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -In recent years, Privacy Preserving Data Mining has emerged as a very active research area. This field of research studies how knowledge or patterns can be extracted from large data stores while maintaining commercial or legislative privacy constraints. Quite often, these constraints pertain to individuals represented in the data stores. While data collectors strive to derive new insights that would allow them to improve customer service and increase sales, consumers are concerned about the vast quantities of information collected about them and how this information is put to use. The question how these two contrasting goals can be reconciled is the focus of this work. We seek ways to improve the tradeoff between privacy and utility when mining data. We address this tradeoff problem by considering the privacy and algorithmic requirements simultaneously, in the context of two privacy models that attracted considerable attention in recent years, k-anonymity and differential privacy. Our analysis and experimental evaluations confirm that algorithmic decisions made with privacy considerations in mind may have a profound impact on the accuracy of the resulting data mining models. 148 pp. Englisch.

Seller Inventory # 9783847303633

Bibliographic Details

• Title: Embedding Privacy in Data Mining
• Publisher: LAP LAMBERT Academic Publishing Dez 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 3847303635
• ISBN 13: 9783847303633
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 238 grams

About this title

Synopsis

In recent years, Privacy Preserving Data Mining has emerged as a very active research area. This field of research studies how knowledge or patterns can be extracted from large data stores while maintaining commercial or legislative privacy constraints. Quite often, these constraints pertain to individuals represented in the data stores. While data collectors strive to derive new insights that would allow them to improve customer service and increase sales, consumers are concerned about the vast quantities of information collected about them and how this information is put to use. The question how these two contrasting goals can be reconciled is the focus of this work. We seek ways to improve the tradeoff between privacy and utility when mining data. We address this tradeoff problem by considering the privacy and algorithmic requirements simultaneously, in the context of two privacy models that attracted considerable attention in recent years, k-anonymity and differential privacy. Our analysis and experimental evaluations confirm that algorithmic decisions made with privacy considerations in mind may have a profound impact on the accuracy of the resulting data mining models.

About the Author

Arik Friedman, PhD: Studied Computer Science at the Technion, Israel Institute of Technology, and MBA with specialization in Technology and Information Systems at Tel-Aviv University. His research interests include privacy, computer security, data mining and machine learning, and how all the above can be combined.