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.
Keywords
Big Data, Columnar Storage, Data Analytics, Encryption, Hadoop, HDFS, Privacy
Article Type
Special Issue Article
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite this Article
Baig, Hidayath Ali
(2024)
"A Column Encryption-Based Privacy-Preserving Framework for Hadoop Big Data Sets,"
Baghdad Science Journal: Vol. 21:
Iss.
5, Article 30.
DOI: https://doi.org/10.21123/bsj.2024.10550
