Data Anonymization Using Map Reduce On Cloud by Using Scalable Two - Phase Top-Down Specialization Approach

A cloud services require in big scale, for users to share a private data such as electronic records and health records, transactional data for analysis of data or mining of that data which bringing privacy concerns. We are using k-anonymity concept for the privacy preservation. Recently data in many cloud applications increases in that accordance with the Big Data style, and it make a challenge for commonly used software tools to manage, capture and process on large-scale data within an elapsed time. So, it is a challenge for existing anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. In this survey paper, we are going to propose and implement a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the Map Reduce framework on cloud. In both phases of our project, we are going to design a group of inventive Map Reduce jobs to concretely accomplish the specialization computation in a highly scalable way.