A Cost-Efficient Privacy-Preserving Clustering Method For Big Data Analysis

Abstract

The era of big data necessitates data analysis methods that are both effective and respectful of privacy. A novel approach to clustering that simultaneously addresses the two issues of cost minimization and privacy preservation is presented in this paper. The proposed strategy use progressed encryption methods and secure multi-party calculation to guarantee information protection all through the bunching system. In addition, it incorporates cost-effective computational strategies to effectively deal with the enormous scale of big data. By adjusting these basic angles, our methodology safeguards touchy data as well as decreases the computational and monetary weights commonly connected with large information investigation. Exploratory outcomes show the adequacy of our technique in keeping up with high bunching precision while altogether bringing down functional expenses. This novel approach provides a solid foundation for privacy-preserving data analysis, making it ideal for applications in fields where data sensitivity and cost constraints are major concerns.