UML Modeling for Preserving Sensitive Information Based on k-Means Clustering Approach

Abstract

This paper proposes the UML modeling approach to facilitate the software engineers in order to implement the cryptographic PPkDC (Privacy Preserving k-means Data Clustering) algorithm based on Object Oriented methodology. Industrial applications for securing sensitive information to be mined have now become the need of different organizations. This paper applies the k-means data clustering approach on to securing sensitive information. The authors have demonstrated the Object Oriented modeling of the proposed approach with the help of visual modeling language platform UML. It applies clustering algorithm on the data encrypted by Pailler homomorphic cryptosystem and UML modeling of the proposed approach has also been given in this research work with the help of different UML sequence, class, activity diagram. Authors have represented the UML approach of software design regarding PPkDC implementation to different data sources. The proposed approach provide the blueprint for constructing and documenting the UML model for the proposed system for providing the cost and time efficient framework for securing sensitive data to be mined accidently or intentionally.