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  1. ; ; ; (, USENIX Association)
    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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    Free, publicly-accessible full text available August 12, 2027
  2. ; ; ; ; ; (, IEEE Transactions on Information Theory)
    Free, publicly-accessible full text available May 1, 2027