Zi-Han Zhao

CPSGD: Differentially Private Stochastic Gradient Descent with Chaotic Post-Processing and Momentum Optimization

Differentially private stochastic gradient descent (DP-SGD) protects training data by adding calibrated Gaussian noise to clipped gradient updates; however, the resulting perturbations inevitably interfere with optimization and reduce classification accuracy. To address this limitation, this paper proposes chaotic po...