Overview
- This book is among the pioneer efforts regarding the development of Association Rule Hiding
- Provides examples throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem
- Covers closely related problems (inverse frequent itemset mining, data reconstruction approaches, etc.), unsolved problems and future directions
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Database Systems (ADBS, volume 41)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (21 chapters)
-
Front Matter
-
Fundamental Concepts
-
Front Matter
-
-
Heuristic Approaches
-
Front Matter
-
-
Border Based Approaches
-
Front Matter
-
-
Exact Hiding Approaches
-
Front Matter
-
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Association Rule Hiding for Data Mining
Authors: Aris Gkoulalas-Divanis, Vassilios S. Verykios
Series Title: Advances in Database Systems
DOI: https://doi.org/10.1007/978-1-4419-6569-1
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Science+Business Media, LLC 2010
Hardcover ISBN: 978-1-4419-6568-4Published: 28 May 2010
Softcover ISBN: 978-1-4614-2605-9Published: 01 July 2012
eBook ISBN: 978-1-4419-6569-1Published: 17 May 2010
Series ISSN: 1386-2944
Edition Number: 1
Number of Pages: XX, 138
Number of Illustrations: 60 b/w illustrations
Topics: Database Management, Information Systems Applications (incl. Internet), Artificial Intelligence, Data Structures and Information Theory, Algorithm Analysis and Problem Complexity, Performance and Reliability