Categorization and Rating the Privacy Maintaining Data Mining Proficiencies by Applying a Data Change–based Model

摘要

In recent years, the data mining proficiencies have met a dangerous challenge due to the altered regardingand concerns of the privacy that is, defending the secrecy of the vital and sore data. Different proficienciesand algorithms have been already demonstrated for Privacy Preserving data mining, which could beassorted in three common approaches: Data modification approach, Data sanitization approach and SecureMulti-party Calculation approach. This paper demonstrates a Data modification– based Framework forcategorization and valuation of the privacy maintaining data mining techniques. Based on our model theproficiencies are divided into two major groups, namely perturbation approach and anonymizationapproach. Also in proposed model, eight functional criteria will be used to examine and analogicallyjudgment of the proficiencies in these two major groups. The suggested framework furnishes a good basisfor 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 describingmodern approaches in this field.