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

Friedman, Arik; Wolff, Ran; Schuster, Assaf

 
9783847303633: Embedding Privacy in Data Mining: Designing Algorithms with Better Privacy and Utility Tradeoffs
Couverture souple
ISBN 10 : 3847303635 ISBN 13 : 9783847303633
Editeur : LAP LAMBERT Academic Publishing, 2011

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.

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Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3847303635
ISBN 13 
9783847303633
Reliure
Broché
Nombre de pages
148
Coordonnées du fabricant
non disponible
Personne responsable
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