Secure electronic medical records in big data field

Abstract

In healthcare industry, medical Big Data has drawn massive newlineattention in medical institutions which include various players widely known newlineas hospitals, clinics, extended care facilities and many other organizations newlineestablished for the practice of medicine. The analytical ability of Big Data can newlinestrengthen abilities of medical institutions in subjects relating to information newlineintegration, analysis and application and also strengthen the efficiency and newlineeffectiveness of medical resource usage. In the trend of Electronic Medical newlineRecords (EMR) management and sharing, understanding the applications of newlinemedical Big Data will be helpful for medical institutions in increasing the newlinebenefits of establishing medical Big Data applications. While transferring newlinethese data, security and privacy issues are magnified by velocity, volume and newlinevariety of Big Data. Traditional security mechanisms are not suitable for Big newlineData security. To overcome these issues, in this work two methods of Big newlineData security methods are analyzed. The efficient Big Data security analysis newlineon Hadoop Distributed File System (HDFS) is based on combination of newlineclustering and data perturbation algorithm using healthcare database. newlineIn the first method, a clustering algorithm is used based on its newlinesimilarity among the various clustering methods and Partition algorithm finds newlinethe clusters altogether as an initial partition of data. Then these partitioned newline