Abstract
As the demand for exercising the "right to be forgotten" grows, the need for verifiable machine unlearning has become increasingly evident to ensure both transparency and accountability. We present {\em zkUnlearner}, the first zero-knowledge framework for verifiable machine unlearning, specifically designed to support {\em multi-granularity} and {\em forgery-resistance}. First, we propose a general computational model that employs a {\em bit-masking} technique to enable the {\em selectivity} of existing zero-knowledge proofs of training for gradient descent algorithms. This innovation enables not only traditional {\em sample-level} unlearning but also more advanced {\em feature-level} and {\em class-level} unlearning. Our model can be translated to arithmetic circuits, ensuring compatibility with a broad range of zero-knowledge proof systems. Furthermore, our approach overcomes key limitations of existing methods in both efficiency and privacy. Second, forging attacks present a serious threat to the reliability of unlearning. Specifically, in Stochastic Gradient Descent optimization, gradients from unlearned data, or from minibatches containing it, can be forged using alternative data samples or minibatches that exclude it. We propose the first effective strategies to resist state-of-the-art forging attacks. Finally, we benchmark a zkSNARK-based instantiation of our framework and perform comprehensive performance evaluations to validate its practicality.
Abstract (translated)
随着人们对于“被遗忘权”的需求增加，确保透明度和问责制的可验证机器遗忘技术的重要性也日益凸显。我们提出了一种名为{\em zkUnlearner}的零知识框架，这是第一个专门用于支持{\em多粒度}和{\em防伪造}的可验证机器遗忘系统。 首先，我们提出了一个通用计算模型，并使用一种称为{\em位掩码}的技术来使现有的针对梯度下降算法训练过程的零知识证明具备选择性。这一创新不仅能够实现传统的基于样本级别的遗忘，还支持更为高级的特征级别和类别级别遗忘。我们的模型可以转换为算术电路，确保与广泛范围内的零知识证明系统兼容。此外，我们方法在效率和隐私方面克服了现有技术的关键限制。 其次，伪造攻击对遗忘过程的可靠性构成严重威胁。特别是在随机梯度下降优化过程中，来自被遗忘数据或包含此类数据的小批量中的梯度可以通过使用不包含这些数据样本或小批量的数据来伪造。我们提出了有效抵抗当前最先进的伪造攻击的战略。 最后，我们采用基于zkSNARK（零知识简洁非交互式知识论证）的实例化框架，并进行了全面性能评估以验证其实际应用价值。
URL
https://arxiv.org/abs/2509.07290
