Authors : V. Shyamala Susan and T. Christopher
Abstract: Privacy preservation in data publishing is the major topic of research in the field of data security. Data publication in privacy preservation provides methodologies for publishing useful information; simultaneously the privacy of the sensitive data has to be preserved. There has been little research addressing how to effectively use the preserved data for data mining in general and for distributed data mining in particular. Here, we propose a new approach for building classifiers using Radial Basis Function (RBF) and Multiple Linear Regression (MLR) by employing sliced data as uncertain data. Use of probability distribution employed in the slicing approach was replaced by classification techniques to enable modeling for sliced data. InRBF, the sliced data is sent into the input layer, the activation function is executed by the hidden lauer and output layer produces classified data. In the same manner, MLR calculates approximate value of one or more sliced data responses on the basis of certain predictors. Results from the experiments show that these techniques show better performance in comparision with other classification approaches.
V. Shyamala Susan and T. Christopher, 2015. Privacy Preserving Data Mining Using Sliced Data for Classification Technique. International Journal of Soft Computing, 10: 468-475.