Electronic Health Records Processing using MapReduce on Cloud
- Chitra R
- Prafulla B G L
- Vernon Louis
Data anonymization, Top-Down Specialization, MapReduce, Privacy Preservation99, Optimized Balanced Scheduling
A cloud services require in big scale, for users to share a private data such as health records, transactional data for analysis of data or mining of that data which bringing privacy concerns. Recently, because of new social behavior, social transformation as well as vast spread of social system many cloud applications increase in accordance with the Big Data style, and make it a challenge for commonly used software tools to manage, capture and process the large-scale data within an elapsed time. In this paper, we are going to implement a scalable two-phase top-down specialization approach to anonymize large scale data sets of electronic health records using the MapReduce framework on cloud. In both phases of our project, we are going to design a group of inventive MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way.
Chitra R, Prafulla B G L, Vernon Louis. "Electronic Health Records Processing using MapReduce on Cloud".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.3, Issue 2, pp.256-259, URL :https://rjwave.org/ijedr/papers/IJEDR1502048.pdf
Volume 3 Issue 2
Pages. 256-259
