A Novel Approach In Privacy Preserving Clustering Process With Cost Minimization With Reference To Big Data
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Abstract
This paper introduces a novel approach in privacy-preserving clustering process with a focus on minimizing costs, particularly in the context of big data. We address the increasing concern over data privacy while considering the resource constraints inherent in processing large datasets. Through a comprehensive literature review, we examine existing techniques in privacy preservation and cost minimization. Subsequently, we propose a methodological framework that integrates privacy-preserving clustering techniques with cost optimization strategies. Our approach aims to maintain data privacy while reducing computational and financial overheads associated with big data analytics. We evaluate the proposed approach through experiments on diverse datasets, analyzing its performance, cost-effectiveness, and scalability. Results demonstrate promising outcomes, indicating the efficacy of our approach in balancing privacy protection and cost efficiency in clustering big data.
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References
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