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Differentially Private Stochastic Gradient Descent (DP-SGD) has become a widely used technique for safeguarding sensitive information in deep learning applications. Unfortunately, DP-SGD’s per-sample gradient clipping and uniform noise addition during training can significantly degrade model utility and fairness. We observe that the latest DP-SGD-Global-Adapt’s average gradient norm is the same throughout the training. Even when it is integrated with the existing linear decay noise multiplier, it has little or no advantage. Moreover, we notice that its upper clipping threshold increases exponentially towards the end of training, potentially impacting the model’s convergence. Other algorithms, DP-PSAC, Auto-S, DP-SGD-Global, and DP-F, have utility and fairness that are similar to or worse than DP-SGD, as demonstrated in experiments. To overcome these problems and improve utility and fairness, we developed the DP-SGD-Global-Adapt-V2-S. It has a step-decay noise multiplier and an upper clipping threshold that is also decayed step-wise. DP-SGD-Global-Adapt-V2-S with a privacy budget of 1 improves accuracy by 0.9795%, 0.6786%, and 4.0130% in MNIST, CIFAR10, and CIFAR100, respectively. It also reduces the privacy cost gap by 89.8332% and 60.5541% in unbalanced MNIST and Thinwall datasets, respectively. Finally, we develop mathematical expressions to compute the privacy budget using truncated concentrated differential privacy (tCDP) for DP-SGD-Global-Adapt-V2-T and DP-SGD-Global-Adapt-V2-S.
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This content will become publicly available on August 12, 2027
From Easy to Hard++: Promoting Differentially Private Image Synthesis Through Spatial-Frequency Curriculum
To improve the quality of differentially private (DP) synthetic images, most studies have focused on improving core optimization techniques such as DP-SGD. Inspired by DP-FETA, this work proposes FETA-Pro, which introduces frequency features as training shortcuts. Their complexity lies between spatial features captured by central images and full images, enabling a finer-grained curriculum for DP training. FETA-Pro uses an auxiliary generator to produce images aligned with noisy frequency features, then trains another model with those images, spatial features, and DP-SGD. Across five sensitive image datasets, FETA-Pro achieves an average of 25.7% higher fidelity and 4.1% greater utility than the best-performing baseline at privacy budget epsilon = 1.
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- Award ID(s):
- 2213700
- PAR ID:
- 10704576
- Publisher / Repository:
- USENIX Association
- Date Published:
- Subject(s) / Keyword(s):
- Differential privacy Image synthesis Synthetic data Curriculum learning DP-SGD
- Format(s):
- Medium: X
- Location:
- Baltimore, MD, USA
- Sponsoring Org:
- National Science Foundation
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