Data Anonymization Using Map Reduce On Cloud by Using Scalable Two - Phase Top-Down Specialization Approach
Abstract: 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.
Keywords: Top Down Specialization, Anonymization of Data, Map Reduce, cloud computing, privacy preservation
How to Cite?: Rahul.S Ransing, M. S. Patole, "Data Anonymization Using Map Reduce On Cloud by Using Scalable Two - Phase Top-Down Specialization Approach", Volume 3 Issue 12, December 2014, International Journal of Science and Research (IJSR), Pages: 1916-1919, https://www.ijsr.net/getabstract.php?paperid=SUB14859, DOI: https://dx.doi.org/10.21275/SUB14859