关于机器遗忘的综述
A Review on Machine Unlearning
AI总结：
本文综述了机器学习模型中的安全与隐私问题，重点介绍了由GDPR等法规引出的机器遗忘概念，分析并讨论了当前的机器遗忘方法及代表性研究成果，以及未来的研究挑战。
摘要：
最近，越来越多的法律开始对用户隐私的可用性进行规范。例如，通用数据保护条例（GDPR）第17条，即“被遗忘权”，要求机器学习应用在用户提出请求时，从数据集中删除部分数据并重新训练模型。此外，从安全角度来看，机器学习模型的训练数据（即可能包含用户隐私的数据）应得到有效保护，包括适当的擦除。因此，研究人员提出了各种隐私保护方法来处理此类问题，即机器遗忘。本文对机器学习模型中的安全和隐私问题进行了深入综述。首先，我们介绍了机器学习如何在日常生活中使用用户的私人数据，以及GDPR在该问题中扮演的角色。然后，我们通过描述机器学习模型中的安全威胁，以及如何利用机器学习平台保护用户隐私免受侵犯，引入了机器遗忘的概念。作为本文的核心内容，我们介绍并分析了当前的机器遗忘方法和几项代表性的研究成果，并在数据谱系的背景下对它们进行了讨论。此外，我们还讨论了该领域未来的研究挑战。
英文摘要：
Recently, an increasing number of laws have governed the useability of users' privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applications to remove a portion of data from a dataset and retrain it if the user makes such a request. Furthermore, from the security perspective, training data for machine learning models, i.e., data that may contain user privacy, should be effectively protected, including appropriate erasure. Therefore, researchers propose various privacy-preserving methods to deal with such issues as machine unlearning. This paper provides an in-depth review of the security and privacy concerns in machine learning models. First, we present how machine learning can use users' private data in daily life and the role that the GDPR plays in this problem. Then, we introduce the concept of machine unlearning by describing the security threats in machine learning models and how to protect users' privacy from being violated using machine learning platforms. As the core content of the paper, we introduce and analyze current machine unlearning approaches and several representative research results and discuss them in the context of the data lineage. Furthermore, we also discuss the future research challenges in this field.
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。
