Implementation of Big Data Privacy Preservation Technique for Electronic Health Records in Multivendor Environment

Abstract

Various diagnostic health data formats and standards include both structured and unstructured data. Sensitive information contained in such metadata requires the development of specific approaches that can combine methods and techniques that can extract and reconcile the information hidden in such data. However, when this data needs to be processed and used for other reasons, there are still many obstacles and concerns to overcome. Modern approaches based on machine learning including big data analytics, assist in the information refinement process for later use of clinical evidence. These strategies consist of transforming various data into standard formats in specific scenarios. In fact, in order to conform to these rules, only de-identified diagnostic and personal data may be handled for secondary analysis, especially when information is distributed or transferred across institutions. This paper proposes big data privacy preservation techniques using various privacy functions. This research focused on secure data distribution as well as security access control to revoke the malicious activity or similarity attacks from end-user. The various privacy preservation techniques such as data anonymization, generalization, random permutation, k-anonymity, bucketization, l-diversity with slicing approach have been proposed during the data distribution. The efficiency of system has been evaluated in Hadoop distributed file system (HDFS) with numerous experiments. The results obtained from different experiments show that the computation should be changed when changing k-anonymity and l-diversity. As a result, the proposed system offers greater efficiency in Hadoop environments by reducing execution time by 15% to 18% and provides a higher level of access control security than other security algorithms.