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Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
Salman Habib, Remi Chou, Taejoon Kim
https://arxiv.org/abs/2508.15523 https://

Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
We propose SPOF (Stabilization of Perturbed Loss Function), a differentially private training mechanism intended for multi-user local differential privacy (LDP). SPOF perturbs a stabilized Taylor expanded polynomial approximation of a model's training loss function, where each user's data is privatized by calibrated noise added to the coefficients of the polynomial. Unlike gradient-based mechanisms such as differentially private stochastic gradient descent (DP-SGD), SPOF does not require inject…