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The main objective of this work is to develop privacy preserving clustering process with cost minimization for Big Data Processing. The aim of privacy preserving calculations is to extricate significant learning from a big amount of information while ensuring data were collected in a delicate way. The basic motive of the current investigation is devoted to the design of an innovative utility based Privacy Preserving Data Mining (PPDM) over big data in cloud systems faced with various hassles like the safety of confidential data and maintenance of the data utility to the feasible extent.