Association Rule Hiding for Data Mining - Couverture rigide

Gkoulalas-Divanis, Aris; Verykios, Vassilios S.

Couverture rigide

ISBN 10 : 1441965688 ISBN 13 : 9781441965684

Editeur : Springer-Verlag New York Inc., 2010

Synopsis

Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique in data mining, which studies the problem of hiding sensitive association rules from within the data.

Association Rule Hiding for Data Mining addresses the problem of "hiding" sensitive association rules, and introduces a number of heuristic solutions. Exact solutions of increased time complexity that have been proposed recently are presented, as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a thorough discussion regarding closely related problems (inverse frequent item set mining, data reconstruction approaches, etc.). Unsolved problems, future directions and specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem.

Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.

Détails de l'édition

• Éditeur: Springer-Verlag New York Inc.
• Date d'édition: 2010
• Langue: anglais
• ISBN 10: 1441965688
• ISBN 13: 9781441965684
• Reliure: Relié
• Nombre de pages: 158
• Coordonnées du fabricant: Springer-Verlag GmbH; customerservice@springernature.com; ; Tiergartenstraße 17; Heidelberg; Baden-Württemberg; 69121; Allemagne