Current models and algorithms proposed for PPDM mainly
focus on how to hide those sensitive information from certain
mining operations. However, as depicted in Fig. 1, the whole
KDD process involve multi-phase operations. Besides the
mining phase, privacy issues may also arise in the phase of
data collecting or data preprocessing, even in the delivery
process of the mining results. In this paper, we investigate
the privacy aspects of data mining by considering the whole
knowledge-discovery process. We present an overview of
the many approaches which can help to make proper use of
sensitive data and protect the security of sensitive information
discovered by data mining. We use the term ``sensitive information''
to refer to privileged or proprietary information that
only certain people are allowed to see and that is therefore
not accessible to everyone. If sensitive information is lost or
used in any way other than intended, the result can be severe
damage to the person or organization to which that information
belongs. The term ``sensitive data'' refers to data from
which sensitive information can be extracted. Throughout the
paper, we consider the two terms ``privacy'' and ``sensitive
information'' are interchangeable.