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This paper introduces DP-NGD, a practical framework that integrates natural gradient descent with differential privacy by decoupling curvature estimation from private data and reconciling isotropic DP constraints with anisotropic second-order optimization, achieving state-of-the-art accuracy and up to 10x convergence speedup under the same privacy budget.