Training and Modeling with Privacy in Network Data Using Machine Learning

Privacy concerns have become paramount in today's data-driven landscape, particularly with the widespread adoption of Internet of Things (IoT) devices. This paper explores the integration of machine learning algorithms, specifically Gaussian Naive Bayes, with privacy-preserving techniques like Differential Privacy to address these concerns. We delve into this innovative approach's principles, methods, and implications. Using a dataset representative of IoT network traffic, we demonstrate how Gaussian Naive Bayes with Differential Privacy can effectively classify data while safeguarding individual privacy, highlighting its significance in pervasive connectivity and data-driven decisionmaking.