Privacy Preserving for Feature Selection in Data Mining Using Centralized Network

This paper proposed a feature selection with privacy preservation in centralized network. Data can be preserved for privacy by perturbation technique as alias name. In centralized data evaluation, it makes data classification and feature selection for data mining decision model which make the structural information of model in this paper. The application of gain ratio technique for better performance of feature selection has taken to perform the centralized computational task. All features don€ t need to preserve the privacy for confidential data for best model. The chi-square testing has taken for the classification of data by centralized data mining model using own processing unit. The alias data model for privacy preserving data mining has taken to develop the data mining technique to make best model without violating the privacy individuals. The proposed process of data miner task has made best feature selection and two type experimental tests have taken in this paper.