Enhancement of data centric security through predictive ridge regression
Shao Dan, Qinyi Zhu · Optimizations in Applied Machine Learning · 2025
Data centric security is becoming increasingly crucial in the digital age, as the volume and importance of data continue to grow exponentially. Current research has focused on developing strategies and technologies to safeguard data, but faces challenges in accurately predicting and preventing security breaches. This paper addresses these challenges by proposing a novel approach using predictive ridge regression to enhance data centric security. By integrating predictive analytics with ridge regression, our research aims to provide a more robust and proactive solution for data security, effectively mitigating risks and optimizing protection measures. Through empirical studies and practical implementations, this paper illustrates the effectiveness and potential of predictive ridge regression in fortifying data security systems, paving the way for future advancements in this critical domain.