Review on Cost Minimization and Big Data: Advancements and Challenges in Privacy Preserving Data Mining (Journal of Advances and Scholarly Researches in Allied Education, 2018)
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.