Embedding Privacy in Data Mining : Designing Algorithms with Better Privacy and Utility Tradeoffs

Language: English

Published by LAP LAMBERT Academic Publishing, 2011

3847303635 / 9783847303633

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9783847303633

Title
Embedding Privacy in Data Mining : Designing Algorithms with Better Privacy and Utility Tradeoffs
Author
Arik Friedman
Publisher
LAP LAMBERT Academic Publishing
Publication year
2011
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3847303635
ISBN 13
9783847303633
Item weight
238 grams
Dimensions
220x150x9 mm

AHA-BUCH GmbH

Einbeck, Germany

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AbeBooks seller since August 14, 2006

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