Association Rule Hiding for Data Mining

  • Aris Gkoulalas-Divanis
  • Vassilios S. Verykios

Part of the Advances in Database Systems book series (ADBS, volume 41)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Fundamental Concepts

    1. Front Matter
      Pages 1-1
    2. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 3-8
    3. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 9-15
    4. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 17-20
    5. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 21-24
    6. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 25-25
  3. Heuristic Approaches

    1. Front Matter
      Pages 28-28
    2. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 29-33
    3. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 35-36
    4. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 37-37
  4. Border Based Approaches

    1. Front Matter
      Pages 40-40
    2. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 41-46
    3. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 47-52
    4. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 53-58
    5. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 59-59
  5. Exact Hiding Approaches

    1. Front Matter
      Pages 62-62
    2. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 63-70
    3. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 71-82
    4. Aris Gkoulalas-Divanis, Vassilios S. Verykios
      Pages 83-92

About this book

Introduction

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 on data mining, which studies the problem of hiding sensitive association rules from within the data.

Association Rule Hiding for Data Mining addresses the optimization problem of “hiding” sensitive association rules which due to its combinatorial nature admits a number of heuristic solutions that will be proposed and presented in this book. Exact solutions of increased time complexity that have been proposed recently are also presented as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a discussion regarding unsolved problems and future directions. 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.

Keywords

Association Gkoulalas Rule Hiding complexity computer computer science currentjm data mining knowledge optimization privacy

Authors and affiliations

  • Aris Gkoulalas-Divanis
    • 1
  • Vassilios S. Verykios
    • 2
  1. 1., Information Analytics LabIBM Research GmbH - ZurichRueschlikonSwitzerland
  2. 2., Department of Computer andUniversity of ThessalyVolosGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-6569-1
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-1-4419-6568-4
  • Online ISBN 978-1-4419-6569-1
  • Series Print ISSN 1386-2944
  • About this book