Data augmentation is widely used to mitigate data bias in the training
dataset. However, data augmentation exposes machine learning models to privacy
attacks, such as membership inference attacks. In this paper, we propose an
effective combination of data augmentation and machine unlearning, which can
reduce data bias while providing a provable defense against known attacks.
Specifically, we maintain the fairness of the trained model with
diffusion-based data augmentation, and then utilize multi-shard unlearning to
remove identifying information of original data from the ML model for
protection against privacy attacks. Experimental evaluation across diverse
datasets demonstrates that our approach can achieve significant improvements in
bias reduction as well as robustness against state-of-the-art privacy attacks.

我们提出了一种有效的数据增强和机器遗忘的组合方法，可以减少数据偏差并提供对已知攻击的可证明防御。具体而言，我们利用基于扩散的数据增强来维护训练模型的公平性，并利用多分片遗忘来从机器学习模型中删除原始数据的识别信息，以保护隐私免受攻击。通过对不同数据集的实验评估表明，我们的方法在减少偏差和抵抗最新隐私攻击方面取得了显著改进。