  We study the Kolmogorov-Arnold Network (KAN), recently proposed as an
alternative to the classical Multilayer Perceptron (MLP), in the application
for differentially private model training. Using the DP-SGD algorithm, we
demonstrate that KAN can be made private in a straightforward manner and
evaluated its performance across several datasets. Our results indicate that
the accuracy of KAN is not only comparable with MLP but also experiences
similar deterioration due to privacy constraints, making it suitable for
differentially private model training.
