JCC_V2_N2_RP3 Shalin Elizabeth S. S. Sarju Journal on Cloud Computing 2350-1308 2 2 31 39 Data Anonymization, Privacy Preservation, Top Down Specialization, MapReduce, Big Data, Cloud Computing In big data applications, data privacy is one of the most important issues on processing large-scale privacy-sensitive data sets, which requires computation resources provisioned by public cloud services.It refers to the commercial "aggregation, mining, and analysis" of very large, complex and unstructured datasets. Due to its large size, discovering knowledge or obtaining pattern from big data within an elapsed time is a complicated task. The cloud and the advances in big data mining and analytics have expanded the scope of information available to businesses, government, and individuals. The internet users also share their private data like health records and financial transaction records for mining or data analysis purpose. For which, data anonymization is used for hiding identity or sensitive intelligence. This paper investigates the problem of big data anonymization for privacy preservation from the perspectives of scalability and cost-effectiveness. Anonymizing large scale data within a short span of time is a challenging task. To overcome that, Enhanced Top –Down Specialization approach (ETDS) can be developed which is an enhancement of Two –Phase Top Down Specialization approach (TPTDS). Accordingly, a scalable and cost-effective privacy preserving framework is developed to provide a holistic conceptual foundation for privacy preservation over big data which enable users to accomplish the full potential of the high scalability, elasticity, and cost-effectiveness of the cloud. The multidimensional anonymization of MapReducing framework will increase the efficiency of the big data processing system. February - April 2015 Copyright © 2015 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=3449