Date: May 27, 2010
Association Rule Hiding for Data Mining
by Aris Gkoulalas-Divanis, IBM Research-Zurich, Switzerland.
Vassilios S. Verykios, University of Thessaly, Greece.
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.
Book series on Advances in Database Systems
1st Edition, 2010 (14 chapters, 150 pages)
Publisher: Springer US