Review on Cost Minimization and Big Data

Advancements and Challenges in Privacy Preserving Data Mining

Abstract

In privacy preserving data mining, preserving the privacy of an individual has been a prime research issue. So as to protect the privacy, different anonymization based approaches were proposed in the writing. The k-obscurity model is one of the basic models utilized for the privacy protection. Be that as it may, it can't give assurance against the trait divulgence. Broadening the possibility of k-obscurity, various anonymization based clustering approaches have been proposed in. It incorporates Byun et al. Eager k-part clustering calculation, Loukides et al. Clustering calculation, Chiu et al. Weighted feature c-implies clustering calculation, Lin et al. One passes k-implies clustering calculation and Kabir et al. Orderly clustering calculated. In this paper we will discuss about the work done in the field of privacy preserving in big data.