  Advances in data collection and data storage technologies have given way to
the establishment of transactional databases among companies and organizations,
as they allow enormous amounts of data to be stored efficiently. Useful
knowledge can be mined from these data, which can be used in several ways
depending on the nature of the data. Quite often companies and organizations
are willing to share data for the sake of mutual benefit. However, the sharing
of such data comes with risks, as problems with privacy may arise. Sensitive
data, along with sensitive knowledge inferred from this data, must be protected
from unintentional exposure to unauthorized parties. One form of the inferred
knowledge is frequent patterns mined in the form of frequent itemsets from
transactional databases. The problem of protecting such patterns is known as
the frequent itemset hiding problem.
  In this paper we present a toolbox, which provides several implementations of
frequent itemset hiding algorithms. Firstly, we summarize the most important
aspects of each algorithm. We then introduce the architecture of the toolbox
and its novel features. Finally, we provide experimental results on real world
datasets, demonstrating the efficiency of the toolbox and the convenience it
offers in comparing different algorithms.
