Classification and Evaluation the Privacy Preserving Data Mining Techniques by Using a Data Modification-Based Framework
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 their framework the techniques are divided into two major groups, namely perturbation approach and anonymization approach.