Privacy Preserving Data Mining

  • Jaideep Vaidya
  • Yu Michael Zhu
  • Christopher W. Clifton

Part of the Advances in Information Security book series (ADIS, volume 19)

Table of contents

About this book

Introduction

Data mining has emerged as a significant technology for gaining knowledge from vast quantities of data. However, concerns are growing that use of this technology can violate individual privacy. These concerns have led to a backlash against the technology, for example, a "Data-Mining Moratorium Act" introduced in the U.S. Senate that would have banned all data-mining programs (including research and development) by the U.S. Department of Defense.

Privacy Preserving Data Mining provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. This book demonstrates how these approaches can achieve data mining, while operating within legal and commercial restrictions that forbid release of data. Furthermore, this research crystallizes much of the underlying foundation, and inspires further research in the area.

Privacy Preserving Data Mining is designed for a professional audience composed of practitioners and researchers in industry. This volume is also suitable for graduate-level students in computer science.

Keywords

classification clustering computer science data mining privacy

Authors and affiliations

  • Jaideep Vaidya
    • 1
  • Yu Michael Zhu
    • 2
  • Christopher W. Clifton
    • 3
  1. 1.Dept. Management Sciences & Information SystemsState Univ. New JerseyNewark
  2. 2.Department of StatisticsPurdue UniversityWest Lafayette
  3. 3.Dept. of Computer SciencePurdue UniversityWest Lafayette

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-29489-6
  • Copyright Information Springer Science+Business Media, Inc. 2006
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-25886-7
  • Online ISBN 978-0-387-29489-6
  • Series Print ISSN 1568-2633
  • About this book