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Ravindra S Hegadi
Solapur University

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Big Data Security Architecture using Split and Merge Method Archana RA; Ravindra S Hegadi; Manjunath T N
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp268-274

Abstract

Due to rapid growth of unstructured data in contemporary information world, there is an essence of big data infrastructure for many applications spread across domains, due to the different source information type and huge volume, data ingestion and data retrieval is important activity during this process data security is a vital to protect user data, in connection with this, authors proposed a big data security architecture using split and merge security method in big data environment using hadoop.This work will help Data security professionals and organizations implementing big data projects.
A Big Data Security using Data Masking Methods Archana R A; Ravindra S Hegadi; Manjunath T N
Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 2: August 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v7.i2.pp449-456

Abstract

Due to Internet of things and social media platforms, raw data is getting generated from systems around us in three sixty degree with respect to time, volume and type. Social networking is increasing rapidly to exploit business advertisements as business demands. In this regard there are many challenges for data management service providers, security is one among them. Data management service providers need to ensure security for their privileged customers in providing accurate and valid data. Since underlying transactional data have varying data characteristics such huge volume, variety and complexity, there is an essence of deploying such data sets on to the big data platforms which can handle structured, semi-structured and un-structured data sets. In this regard we propose a data masking technique for big data security. Data masking ensures proxy of original dataset with a different dataset which is not real but looks realistic. The given data set is masked using modulus operator and the concept of keys. Our experiment advocates enhanced modulus based data masking is better with respect to execution time and space utilization for larger data sets when compared to modulus based data masking. This work will help big data developers, quality analysts in the business domains and provides confidence for end-users in providing data security.
Co-Authors Archana R A Archana RA Manjunath T N Manjunath T N