Enhanced Secured Map Reduce layer for Big Data privacy and security

Abstract

The publication and dissemination of raw data are crucial elements in commercial, academic, and medical applications. With an increasing number of open platforms, such as social networks and mobile devices from which data may be collected, the volume of such data has also increased over time move toward becoming as Big Data. The traditional model of Big Data does not specify any level for capturing the sensitivity of data both structured and unstructured. It additionally needs to incorporate the notion of privacy and security where the risk of exposing personal information is probabilistically minimized. This paper introduced security and privacy layer between HDFS and MR Layer (Map Reduce) known as new proposed Secured Map Reduce (SMR) Layer and this model is known as SMR model. The core benefit of this work is to promote data sharing for knowledge mining. This model creates a privacy and security guarantee, resolve scalability issues of privacy and maintain the privacy-utility tradeoff for data miners. In this SMR model, running time and information loss have a remarkable improvement over the existing approaches and CPU and memory usage are also optimized.