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
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
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.
This book is among the pioneer efforts regarding the development of Association Rule HidingProvides examples throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problemCovers closely related problems (inverse frequent itemset mining, data reconstruction approaches, etc.), unsolved problems and future directions