神经网络中的轻量级机器遗忘(CS)

近年来，机器学习神经网络已经深入到人们的生活中。作为便利的代价，人们的个人信息也有被泄露的风险。及时引入了“被遗忘权”，规定个人有权根据自己的同意撤回对个人信息处理活动的同意。为了解决这一问题，提出了机器遗忘的方法，该方法允许模型删除所有存储的私人信息。以往的研究，包括再训练和增量学习来更新模型，往往占用额外的存储空间或难以应用于神经网络。我们的方法只需要对目标模型的权值做一个小的扰动，使其在用剩余数据子集训练的模型的方向上迭代，直到遗忘数据对模型的贡献完全消除。本文在5个数据集上进行的实验证明了该方法的有效性，比再训练算法快15倍。

原文题目：Lightweight machine unlearning in neural network

原文：In recent years, machine learning neural network has penetrated deeply into people's life. As the price of convenience, people's private information also has the risk of disclosure. The "right to be forgotten" was introduced in a timely manner, stipulating that individuals have the right to withdraw their consent from personal information processing activities based on their consent. To solve this problem, machine unlearning is proposed, which allows the model to erase all memory of private information. Previous studies, including retraining and incremental learning to update models, often take up extra storage space or are difficult to apply to neural networks. Our method only needs to make a small perturbation of the weight of the target model and make it iterate in the direction of the model trained with the remaining data subset until the contribution of the unlearning data to the model is completely eliminated. In this paper, experiments on five datasets prove the effectiveness of our method for machine unlearning, and our method is 15 times faster than retraining.