  In recent years, the data mining techniques have met a serious challenge due
to the increased concerning and worries of the privacy, that is, protecting the
privacy of the critical and sensitive data. Different techniques and algorithms
have been already presented for Privacy Preserving data mining, which could be
classified in three common approaches: Data modification approach, Data
sanitization approach and Secure Multi-party Computation approach. This paper
presents a Data modification- based Framework for classification and evaluation
of the privacy preserving data mining techniques. Based on our framework the
techniques are divided into two major groups, namely perturbation approach and
anonymization approach. Also in proposed framework, eight functional criteria
will be used to analyze and analogically assessment of the techniques in these
two major groups. The proposed framework provides a good basis for more
accurate comparison of the given techniques to privacy preserving data mining.
In addition, this framework allows recognizing the overlapping amount for
different approaches and identifying modern approaches in this field.
