DP-RVFL: A Differentially Private Randomized-Feature Neural Model for Privacy–Utility Efficient Learning

Differentially private stochastic gradient descent (DP-SGD) enables neural network training with formal privacy guarantees, but its utility depends on model architecture, data modality, and privacy budget. DP-RVFL freezes random hidden features and privately trains an output layer with a direct input connection. We compare it with Dense/CNN models, no-link and parameter-matched frozen controls, logistic regression and published DP-ELM. MNIST, CIFAR-10, Cardio and Heart are evaluated at ε = 0.1–8 using five training seeds. Gaussian gradient models use Poisson sampling, Adam, δ = 10−5 and add/remove-one adjacency. Primary ε = 2 accuracies are 90.30%, 38.61%, 72.99% and 63.48%, respectively. CIFAR-10 training takes 71.29 s versus 529.76 s for DP-CNN using eight CPU threads. DP-RVFL exceeds the no-link mean accuracy in 20 of 24 primary conditions, but the matched frozen control in only eight. Two additional executions preserve the same configuration and are reported separately. Five-pair exact tests do not establish statistical superiority. Diagnostics do not substantiate a reduced-clipping explanation, and Heart results vary across small partitions. The evidence supports lower training time relative to the tested Dense/CNN models; privacy budgets apply per model conditional on public benchmark preparation.