Kavli Affiliate: Salman Habib
| First 5 Authors: Salman Habib, Salman Habib, , ,
| Summary:
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 injecting noise into the gradients of
the loss function, which improves both computational efficiency and stability.
This formulation naturally supports simultaneous privacy guarantees across all
users. Moreover, SPOF exhibits robustness to environmental noise during
training, maintaining stable performance even when user inputs are corrupted.
We compare SPOF with a multi-user extension of DP-SGD, evaluating both methods
in a wireless body area network (WBAN) scenario involving heterogeneous user
data and stochastic channel noise from body sensors. Our results show that SPOF
achieves, on average, up to 3.5% higher reconstruction accuracy and reduces
mean training time by up to 57.2% compared to DP-SGD, demonstrating superior
privacy-utility trade-offs in multi-user environments.
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