班级小丑：企业规模的机器遗忘中的数据编辑
Class Clown: Data Redaction in Machine Unlearning at Enterprise Scale
摘要：
通过最近的数据隐私法，如《一般数据保护条例》和《加利福尼亚消费者隐私法》，个人对个人数据的控制权越来越大。这些法律的一个方面是要求企业删除私人信息的能力，即所谓的“被遗忘权”或“删除权”。这些法律对使用这些有价值的消费者数据集来训练大型、高度准确的深度神经网络（DNN）的公司和组织具有严重的财务影响。然而，收到的编辑请求对如何在履行核心业务的同时遵守法律提出了复杂的技术挑战。我们引入了一个DNN模型生命周期维护过程，该过程确定了如何处理特定的数据编辑请求，并最大限度地减少了完全重新训练模型的需要。我们的过程是基于成员推断攻击作为一个合规工具的训练集中的每一个点。这些攻击模型量化了所有训练数据点的隐私风险，并形成了准确部署模型的后续数据编辑的基础;切除是通过增量模型更新中的不正确标签分配实现的。
英文摘要：
Individuals are gaining more control of their personal data through recent data privacy laws such the General Data Protection Regulation and the California Consumer Privacy Act. One aspect of these laws is the ability to request a business to delete private information, the so called "right to be forgotten" or "right to erasure". These laws have serious financial implications for companies and organizations that train large, highly accurate deep neural networks (DNNs) using these valuable consumer data sets. However, a received redaction request poses complex technical challenges on how to comply with the law while fulfilling core business operations. We introduce a DNN model lifecycle maintenance process that establishes how to handle specific data redaction requests and minimize the need to completely retrain the model. Our process is based upon the membership inference attack as a compliance tool for every point in the training set. These attack models quantify the privacy risk of all training data points and form the basis of follow-on data redaction from an accurate deployed model; excision is implemented through incorrect label assignment within incremental model updates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
