Application of Reduction Algorithms for Enhancing Data Security in Biotechnological Research and Production

Abstract

With the rapid expansion of biotechnological innovations, ensuring the integrity and security of data has become paramount. This is particularly critical in biotechnology sectors where data breaches can not only compromise intellectual property but also impact regulatory compliance and patient safety. In this context, the assessment of network information security posture is crucial. Traditional methods of extracting critical security elements are often inefficient and heavily dependent on specific data sets. In contrast, the parsimony algorithm, known for its simplicity, speed, and stability, offers a promising approach for data dimensionality reduction in sensitive environments. This study explores the application of the parsimony algorithm to enhance data security in biotechnological research and production settings. By examining its effectiveness in extracting key security elements, this research aims to provide robust support for security risk assessment and prediction within the biotech industry. The outcomes are expected to offer a theoretical foundation and practical guidelines that could be pivotal for future research and the safeguarding of biotechnological data.