Assessing the Impact of Differential Privacy on Model Performance in Cybersecurity Applications

Abstract

This paper examines the implications of implementing differential privacy in cybersecurity applications, particularly focusing on its effects on model performance. As data privacy concerns rise, cybersecurity systems must balance privacy and effectiveness. We conduct a series of experiments using different machine learning models to evaluate the trade-offs associated with differential privacy techniques. Our findings demonstrate the nuances of adopting differential privacy in cybersecurity contexts, providing insights into optimal configurations for maintaining both privacy and performance.