Provisioning A Positive Cluster Associative Schema for Privacy Preservation in the Mining Paradigm

Abstract

The considerable data age has seen an exponential rise in personal data due to the widespread use of mobile devices and the Internet. Since important information is extracted throughout the data mining process, there are significant privacy concerns regarding the network user data. Adding random noise to safeguard sensitive data while preserving certain statistical aspects is called privacy preservation, a novel paradigm that operates independently of the attackers' prior knowledge. Expanding upon the proposed technique for network user data and robust privacy, this research proposes a multiple cores positive cluster associative schema (PCAS-PP) with privacy preservation. During the data mining, you can better optimize data clustering and take advantage of the privacy leakage problem. Researchers do a thorough theoretical study and run simulations to assess our schema. The findings demonstrate that our schema is more accurate, efficient, and protects privacy than earlier schemas. Some other metrics like run time, F-measure and cluster ratio of three datasets are evaluated and compared.