Managing Privacy of Sensitive Attributes Using MFSARNN Clustering with Optimization Technique

Abstract

Several agencies, institutions and organizations publish the sensitive data for the public to achieve better research result.Privacy preserving and security in data publishing are the major challenge.Thus the focus of this paper is to ensure privacy and security of sensitive data by slicing algorithm. The slicing algorithm partition the data into vertical and horizontal columns. The attributes and tuples in the slicing algorithm is clustered based on their similarity. In vertical partitioning, the attributes are grouped by Modified Fully Self Adaptive Resonance Neural Networks (MFSARNN). The cluster formation has been improved by Genetic Algorithm based feature selection. In the horizontal partition, the tuple sare grouped by Metaheuristic Fireflies Algorithm with Minkowsi Distance Measure (MFAMD). In this way the proposed system overcomes the privacy threats such as Identity disclosure, Attribute disclosure and Membership disclosure. The experimental result with respect to mean square error, clustering rate is used to analyse the privacy of the system.
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