Development of Privacy Preserving Clustering Process with Cost Minimization for Big Data Processing - Chitra, S; Bharanidharan, R (International Journal of Computer Sciences and Engineering, 2025)
Unfathomable quantum of comprehensive private data is habitually gathered as the mutual exchange of the corresponding information has come as a shot in arm for a multitude of data mining applications. The related data extensively encompass the shopping trends, criminal records, medical history, credit records and so forth. It is true that the corresponding information has proved its mettle as a vital asset to the business entities and governmental organization for the purpose of taking prompt and perfect decisions by means of assessing the pertinent records. However, it has to be borne in mind that harsh privacy. With an eye on effectively addressing the corresponding thorny issues, in this document, an earnest endeavor is made to kick-start a novel clustering Probabilistic Possibility Fuzzy C Means Clustering (PFCM) approach viz. The Big data processing, in fact, involves the explosive expansion of demands on evaluation, storage, and transmission in data centers, thus leading to incredible working expenses to be borne by the data center providers. To achieve this, we introduce VSSFA and Map Reduce Framework in Cloud environment. In this thesis we deeply develop a privacy preserving clustering process with cost minimization for big data processing.