Data Anonymization Using Map Reduce on Cloud based A Scalable Two-Phase Top-Down Specialization

Abstract

A large number of cloud services require users to impart` private data like electronic health records for data analysis or Mining, bringing privacy concerns. Anonymizing information sets through generalization to fulfill certain security prerequisites, for example, k-anonymity is a broadly utilized classification of protection safeguarding procedures At present, the scale of information in numerous cloud applications increments immensely as per the Big Data pattern, in this manner making it a test for normally utilized programming instruments to catch, oversee, and process such substantial scale information inside a bearable slipped by time. As an issue, it is a test for existing anonymization methodologies to accomplish security protection on security touchy extensive scale information sets because of their inadequacy of adaptability. In this paper, we propose a versatile two-stage top-down specialization (TDS) methodology to anonymize huge scale information sets utilizing the Map reduce schema on cloud. Experimental evaluation results demonstrate that with our approach, the scalability and efficiency of TDS can be significantly improved over existing approaches.