Improved big data privacy and security using hybrid elliptic curve cryptography with pillar K means clustering algorithm in secured map reduce layer

Abstract

newline The traditional Big Data framework does not clearly state that both the structured and unstructured data sensitivity like health care, personal information data, online transaction data and so on. In addition, the concept of privacy and security of Big Data needs to be incorporated in the cluster nodes of the Map Reduce Layer in Hadoop framework, which is vulnerable and prone to be attacked. Accordingly, the threat of revealing personal data is subsequently alleviated in recent years. The proposed work Secured Map Reduce Layer (SMR) in the Big Data framework for improving the security and privacy of sensitive data through hybrid approach by integrating PSO (Particle Swarm Optimization) and Elliptic Curve Cryptographic mechanism. This proposed framework deals with, choosing the optimal private key for authentication, using Particle Swarm Optimization and protecting the data by Cryptographic mechanism of Elliptic Curve Cryptography. Particle swarm Optimization authorizes the global best fitness value to find the optimal private key with efficient authentication. The proposed methodology focuses on the storage of Big Data. In the first phase, Hadoop Distributed File System is used to store huge data in efficient ways and ensuring security and preserve privacy while data is processing in Map Reduce Layer. Efficient storage in HDFS is achieved by applying clustering for segregating the related data and group the similar data into blocks, which are stored in distributed nodes of HDFS for the ease of processing the data and preserving privacy. In this study, Semi Structured Medical Transcription text dataset is used. The initial step of clustering is text preprocessing to polish the text data to enhance the cluster outcomes by removing the noise. Traditional text pre-processing techniques like stop word removal, tokenization and stemming are applied in the dataset. The next step to convert the text data into numerical format for clustering is using tf-idf (term frequency-inverse document frequency) method.