Development Of Data Security Algorithms: A Literature Review On Information Security In The Context Of Big Data - Nathanael David Christian Barus; Natasha Fedora Barus (STIKes Ibnu Sina Ajibarang, 2024)
In the era of Big Data, securing sensitive information and ensuring data integrity have become paramount concerns due to the unprecedented volume and intricacy of data. Traditional security algorithms face significant challenges in adapting to the distinct characteristics of Big Data. This literature review explores the evolution of data security algorithms tailored explicitly for the Big Data landscape, aiming to address the increasing demand for robust security solutions capable of handling the unique challenges posed by the massive scale and complexity of data. By scrutinizing existing literature, the review unveils advancements, trends, and innovations developed by researchers and practitioners to mitigate vulnerabilities associated with handling vast datasets. The review also sheds light on emerging technologies and cryptographic techniques specifically designed for Big Data security, contributing to enhanced confidentiality, integrity, and availability in the face of evolving cyber threats. While these developments offer advantages such as improved data protection and threat detection, the review highlights challenges, including algorithmic bias, computational complexity, privacy trade-offs, and a shortage of skilled workforce. By considering these factors and emphasizing continuous improvement and ethical considerations, organizations can responsibly leverage data security algorithms to enhance information security in the era of Big Data.