In the context of machine unlearning, the primary challenge lies in effectively removing traces of private data from trained models while maintaining model performance and security against privacy attacks like membership inference attacks. Traditional gradient-based unlearning methods often rely on extensive historical gradients, which becomes impractical with high unlearning ratios and may reduce the effectiveness of unlearning. Addressing these limitations, we introduce Mini-Unlearning, a novel approach that capitalizes on a critical observation: unlearned parameters correlate with retrained parameters through contraction mapping. Our method, Mini-Unlearning, utilizes a minimal subset of historical gradients and leverages this contraction mapping to facilitate scalable, efficient unlearning. This lightweight, scalable method significantly enhances model accuracy and strengthens resistance to membership inference attacks. Our experiments demonstrate that Mini-Unlearning not only works under higher unlearning ratios but also outperforms existing techniques in both accuracy and security, offering a promising solution for applications requiring robust unlearning capabilities.

翻译：在机器遗忘领域，主要挑战在于有效移除训练模型中私有数据的痕迹，同时保持模型性能并抵御成员推理攻击等隐私攻击。传统的基于梯度的遗忘方法通常依赖大量历史梯度，这在遗忘率较高时变得不切实际，并可能降低遗忘效果。针对这些局限性，我们提出了一种新颖方法Mini-Unlearning，其基于一个关键发现：遗忘参数通过压缩映射与重新训练参数相关联。我们的Mini-Unlearning方法利用最小历史梯度子集，并借助该压缩映射实现可扩展的高效遗忘。这种轻量级可扩展方法显著提升了模型精度，并增强了对成员推理攻击的抵抗能力。实验表明，Mini-Unlearning不仅能在更高遗忘率下工作，而且在精度和安全性方面均优于现有技术，为需要强大遗忘能力的应用提供了有前景的解决方案。