Association Rule Hiding for Data Mining
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 hid
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ADVANCES IN DATABASE SYSTEMS Volume 411
Series Editors Ahmed K. Elmagarmid Purdue University West Lafayette, IN 47907
Amit P. Sheth Wright State University Dayton, OH 45435
For other titles published in this series, please visit www.springer.com/series/5573
Association Rule Hiding for Data Mining
by
Aris Gkoulalas-Divanis
IBM Research GmbH - Zurich, Rueschlikon, Switzerland
Vassilios S. Verykios University of Thessaly, Volos, Greece
Aris G koulalas-Divanis Information Analytics Lab IBM Research GmbH - Zurich Saumerstrasse 4 8803 Rueschlikon Switzerland [email protected]
Vassilios S. Verykios Department of Computer and Communication Engineering University of Thessaly Glavani 37 & 28th Octovriou Str. GR 38221 Volos Greece [email protected]
ISSN 1386-2944 ISBN 978-1-4419-6568-4 e-ISBN 978-1-4419-6569-1 DOI 10.1007/978-1-4419-6569-1 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010927402 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Dedicated to my parents, Aspa and Dimitris. — Aris Gkoulalas–Divanis.
Dedicated to Jenny and Aggelos. — Vassilios S. Verykios.
Preface
Since its inception, privacy preserving data mining has been an active research area of increasing popularity in the data mining community. This line of research investigates the side-effects of the existing data mining technology that originate from the penetration into the privacy of individuals and organizations. From a general point of view, privacy issues related to the application of data mining can be classified into two main categories, namely data hiding and knowledge hiding. Data hiding methodologies are related to the data per se, aiming to remove confidential or private information from the data prior to its publication. Knowledge hiding methodologies, on the other hand, are concerned with the sanitization of data leading to the disclosure of confidential and private knowledge, when the data is mined by the existing data mining tools for knowledge patterns. In this book, we provide an extensive survey on a specific class of privacy preserving data mining methods that belong to the knowledge hiding thread and are collectively known as association rule hiding methods. “Association
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