Categorization and Rating the Privacy Maintaining Data Mining Proficiencies by Applying a Data Change-Based Model

In recent years, the data mining proficiencies have met a dangerous challenge due to the altered regarding and concerns of the privacy that is, defending the secrecy of the vital and sore data. Different proficiencies and algorithms have been already demonstrated for Privacy Preserving data mining, which could be assorted in three common approaches: Data modification approach, Data sanitization approach and Secure Multi-party Calculation approach. This paper demonstrates a Data modification- based Framework for categorization and valuation of the privacy maintaining data mining techniques. Based on our model the proficiencies are divided into two major groups, namely perturbation approach and anonymization approach. Also in proposed model, eight functional criteria will be used to examine and analogically judgment of the proficiencies in these two major groups. The suggested framework furnishes a good basis for more accurate comparison of the given proficiencies to privacy maintaining data mining. In addition, this framework permits distinguishing the overlapping quantity for different approaches and describing modern approaches in this field.