Enhancing Privacy Preservation in Data Mining using Cluster based Greedy Method in Hierarchical Approach
R. Hariharan, C. Mahesh, P. Prasenna, Ravi Kumar · Indian Journal of Science and Technology · 2016
Background/Objectives: Privacy preservation in data mining to hold back the sensitive data from attackers. Findings: There are various existing methods available to preserve the data like perturbation, anonymization, randomization etc., each method has its own advantages and disadvantages. The trade-off between security and utility of data should be handled with standardizing methods for the PPDM. In this paper explained a method based on PPDM in data mining using cluster based greedy method. Application/Improvements: This method can be applied in sensitive data areas such as hospitals, Customer Management System, government survey, etc., where there is need for privacy preservation. Keywords: Cluster based Greedy Method, Classification Error, Isometric Transformation, Privacy Preservation, Privacy Preservation Rate