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.
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