Zahi explains the concept of differential privacy, emphasizing the importance of adding noise to data sets to protect individual data points during model training. The discussion highlights the counterintuitive nature of privacy risks in large datasets, particularly in deep learning models, where parameters could inadvertently reveal sensitive information.
