Power Industry Big Data Privacy Protection Processing Method Based on Fuzzy Logic and Intelligent Clustering

Abstract

The power industry is the corporate globe’s backbone, delivering vital energy to industrial, manufacturing, promotional, and residential clients worldwide. Investment has been prompted by the shift of fuel and energy sources, rising environmental laws, and an ageing generating fleet and transmission infrastructure in industrialized nations with mature power systems. The evolving requirement in the power industry is causing several workforce challenges, including massive shifts in skills required, a skills gap for delivering. Performing newer technologies, changes occur when the Industry is experiencing high retirements and challenges recruiting a strengthening workforce. In this paper, Big Data based on the fuzzy logic method has been suggested to protect sensitive information. BD-FLM proposes a large data clustering-based privacy preservation probabilistic model that aims to cause the least disruption while maintaining the greatest amount of privacy. To alter or generalize sensitive data, use a methodology that secures sensitive information after detecting sensitive data clusters. In terms of conventional performance assessment metrics, the model’s privacy protection of individual data in large data with little disruption and effective reconstruction underlines its importance. This paper can automatically cluster based on the power characteristic factor and efficiently identify the power-related aspects of distinct user groups. The simulation results demonstrate that the proposed technique outperforms the comparison algorithm regarding prediction accuracy. As a result of anticipating the accurate values, individual privacy is preserved, but data accuracy is improved.