Classificationandevaluation the Privacy Preserving Distributed Data Miningtechniques

In recent years, the data mining techniques invarious areas have met serious challenges increasingconcernsaboutprivacy. Different techniques and algorithms have been already presented for Privacy preserving data mining (PPDM), which could be classified in two scenarios: centralized data scenario and distributed data scenario. This paper presents a Framework for classification and evaluation of the privacy preserving data mining techniques for distributed data scenario. Based on our framework the techniques are divided intothree major groups, namely Secure Multiparty Computation based techniques, Secret Sharing based techniques and Perturbation based techniques.Also in proposed framework, seven functional criteria will be used to analyze and analogically evaluation of the techniques in these three major groups. The proposed framework provides a good basis for more accuratecomparison of the given techniques to privacy preserving distributed data mining. In addition, this framework allows recognizing the overlapping amount for different approaches and identifying modern approaches in this field.