Abstract
Traditional data masking techniques such as anonymization cannot achieve the expected privacy protection while ensuring data utility for privacy-preserving machine learning. Synthetic data plays an increasingly important role as it generates a large number of training samples and prevents information leakage in real data. The existing methods suffer from the repeating trade-off processes between privacy and utility. We propose a novel framework for differential privacy generation, which employs an Error Feedback Stochastic Gradient Descent(EFSGD) method and introduces a reconstruction loss and noise injection mechanism into the training process. We generate images with higher quality and usability under the same privacy budget as the related work. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both grayscale and RGB images. We achieve state-of-the-art results over almost all metrics on three benchmarks: MNIST, Fashion-MNIST, and CelebA.
Abstract (translated)
传统的数据屏蔽技术，如匿名化，在确保隐私保护的同时无法实现预期的数据效用，这对于隐私保护的机器学习来说是一个挑战。合成数据因其能够生成大量训练样本并防止真实数据的信息泄露而扮演着越来越重要的角色。现有的方法在隐私和实用性之间反复进行权衡过程。我们提出了一种新颖的微分隐私生成框架，该框架采用误差反馈随机梯度下降（EFSGD）方法，并在训练过程中引入了重建损失和噪声注入机制。与相关工作相比，在相同的隐私预算下，我们的框架能够生成质量更高且更具实用性的图像。 广泛的实验展示了我们提出的框架对于灰度图和RGB图像的有效性和泛化能力。我们在三个基准数据集（MNIST、Fashion-MNIST 和 CelebA）上实现了几乎所有的指标上的最新成果。
URL
https://arxiv.org/abs/2601.15061
