An experimental technique on potential issues and prospective solution for preserving privacy in big data

Abstract

Big Data is extremely a large amount of unstructured data coming from different sources along with high speed and is highly defined by 4 V’s that are volume, velocity, variety and value. Big data cannot be handled by conventional methods as they are meant for small structured datasets which are incapable in storing and processing large datasets. In present scenario, Hadoop, Storm, Spark, Flink etc. are certain frameworks which are proposed for storing and processing the data speedily. Big data contains variety of data including person- specific information. This personal information needs to be preserved otherwise publishing data may put the individual’s privacy at risk. Keeping this in view, various anonymity principles, privacy preserving techniques and metrics had been reviewed. Therefore, the premise of the present review work is to elaborate potential issues and prospective solutions for privacy preservation in person-specific information in big data environment. Taking privacy into consideration, this paper reviews various anonymity principles, its techniques and metrics. The objective of this paper is to provide some privacy issues and its perspectivesolutions.