A Column Encryption-Based Privacy-Preserving Framework for Hadoop Big Data Sets

Main Article Content

Hidayath Ali Baig
https://orcid.org/0000-0002-1953-6871

Abstract

The exponential growth of the Internet, the Internet of Things, and Cloud Computing in recent times has led to a significant rise of data across various sectors of business and industry. Big data has become a growing trend in recent years, attracting the attention of academics, corporate leaders, and government officials worldwide. Hadoop is a commonly adopted framework for processing big data. This data expansion has the potential to provide substantial and beneficial advantages, and some early success has been achieved from a technical standpoint in dealing with such a large quantity of data. Along with its many benefits, it also has a slew of disadvantages. These include, but are not limited to, data storage, exchange, curation, transit, analysis, visualization, security and privacy. In this research, the privacy implications of Big Data analytics are being investigated. Several publications suggest methods to secure big data. Each technique has advantages and disadvantages. Regardless of privacy laws, application developers must protect sensitive data. Therefore, there is need for innovative methods to guarantee the protection of individuals' privacy in the context of big data. This paper presents a framework for preserving privacy in data-at-rest within the Hadoop architecture. The framework employs columnar data storage, data masking, and encryption techniques to address these challenges efficiently.

Article Details

How to Cite
1.
A Column Encryption-Based Privacy-Preserving Framework for Hadoop Big Data Sets. Baghdad Sci.J [Internet]. 2024 May 25 [cited 2024 Jun. 17];21(5(SI):1798. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10550
Section
Special Issue - (ICCDA) International Conference on Computing and Data Analytics

How to Cite

1.
A Column Encryption-Based Privacy-Preserving Framework for Hadoop Big Data Sets. Baghdad Sci.J [Internet]. 2024 May 25 [cited 2024 Jun. 17];21(5(SI):1798. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10550

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Baig HA, Jummani DF, Ali SZ. A Framework for Preserving the Privacy of Data in Hadoop Clusters using Column Encryption. Int. J. Adv. Res. Eng. Technol. 2021; 8: 17894-902.

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