验证机器去学习是脆弱的
Verification of Machine Unlearning is Fragile
AI总结：
本文探讨了机器去学习验证的脆弱性，提出两种对抗性去学习过程以绕过现有验证策略，揭示了验证方法的局限性。
摘要：
随着机器学习领域隐私问题的加剧，数据所有者现在可以利用机器去学习技术将数据从模型中移除，以应对最近的立法要求。为提高机器去学习的透明度并防止模型提供者可能的不诚实行为，已提出各种验证策略，使数据所有者能够确认其目标数据是否已有效从模型中去除。然而，我们对机器去学习验证的安全问题仍了解甚少。本文探讨了一个新的研究问题：模型提供者是否可以在保留所谓已去学习的数据信息的同时绕过验证策略。我们的研究得出悲观的结论：机器去学习的验证是脆弱的。具体来说，我们将当前的验证策略根据模型提供者可能的不诚实行为分为两类。随后，我们引入了两种新的对抗性去学习过程，能够绕过这两种类型。我们通过理论分析和使用真实世界数据集的实证实验验证了我们方法的有效性。本研究突显了机器去学习验证的脆弱性和局限性，为进一步研究机器去学习的安全性铺平了道路。
英文摘要：
As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, following recent legislation. To enhance transparency in machine unlearning and avoid potential dishonesty by model providers, various verification strategies have been proposed. These strategies enable data owners to ascertain whether their target data has been effectively unlearned from the model. However, our understanding of the safety issues of machine unlearning verification remains nascent. In this paper, we explore the novel research question of whether model providers can circumvent verification strategies while retaining the information of data supposedly unlearned. Our investigation leads to a pessimistic answer: \textit{the verification of machine unlearning is fragile}. Specifically, we categorize the current verification strategies regarding potential dishonesty among model providers into two types. Subsequently, we introduce two novel adversarial unlearning processes capable of circumventing both types. We validate the efficacy of our methods through theoretical analysis and empirical experiments using real-world datasets. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
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。
