Machine learning based business intelligence security and privacy analysis with gaming model in training complexity application.

Zhao, L., & Zhang, J. (2024, May). Machine learning based business intelligence security and privacy analysis with gaming model in training complexity application. Entertainment Computing, 50, Article 100695. https://doi.org/10.1016/j.entcom.2024.100695

In this scholarly article, Zhao and Zhang present a technical investigation into the security and privacy vulnerabilities of business intelligence systems that utilize machine learning. The authors employ a gaming model to simulate potential attack and defense scenarios, analyzing how the complexity of model training impacts the system's susceptibility to data breaches and adversarial attacks. The research highlights the tension between maximizing the predictive power of Artificial Intelligence and maintaining robust data protection standards. By quantifying training complexity, the study offers a new methodology for evaluating the trade-offs between performance and security in corporate environments. The findings suggest that as business intelligence systems become more sophisticated, specialized security protocols must be integrated directly into the training phase to safeguard sensitive information. This article is a significant contribution to the field of computer science, providing rigorous mathematical analysis for experts interested in the intersection of Artificial Intelligence, cybersecurity, and data privacy.