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Enhancing Privacy Preservation in Data Mining using Cluster based Greedy Method in Hierarchical Approach
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
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