无需原始数据即可安全删除模型中的特定信息，保护隐私同时保持模型性能。
Shadow Unlearning: A Neuro-Semantic Approach to Fidelity-Preserving Faceless Forgetting in LLMs
- 用匿名化数据实现近似遗忘，避免暴露个人敏感信息。
- 在多个大模型上验证，遗忘效果优于传统方法，计算效率提升10倍以上。
- 适合注重隐私保护且需维持模型可用性的应用场景。
机器遗忘旨在有选择地移除特定训练样本的影响，以满足如GDPR的‘被遗忘权’等隐私法规要求。然而，现有许多方法需要访问待删除的数据，存在成员推断攻击风险及个人身份信息（PII）泄露隐患。本文提出一种新型近似遗忘范式——影子遗忘（Shadow Unlearning），可在不暴露原始数据的前提下，对匿名化的遗忘数据执行机器遗忘。为此，我们设计了新的隐私保护框架——神经语义投影遗忘（NSPU）。为评估该方法，我们在五个不同领域构建了多领域虚构遗忘（MuFU）数据集，并引入评估栈以量化知识保留与遗忘效果之间的权衡。在多种大语言模型上的实验表明，NSPU实现了更优的遗忘性能，有效保留模型效用，显著增强用户隐私保护。此外，该方法的计算效率至少比标准遗忘方法高10倍。研究结果推动了面向隐私保护的机器遗忘新方向，在数据安全与模型保真度间取得平衡。
原文摘要 · Abstract (English)
Machine unlearning aims to selectively remove the influence of specific training samples to satisfy privacy regulations such as the GDPR's 'Right to be Forgotten'. However, many existing methods require access to the data being removed, exposing it to membership inference attacks and potential misuse of Personally Identifiable Information (PII). We address this critical challenge by proposing Shadow Unlearning, a novel paradigm of approximate unlearning, that performs machine unlearning on anonymized forget data without exposing PII. We further propose a novel privacy-preserving framework, Neuro-Semantic Projector Unlearning (NSPU) to achieve Shadow unlearning. To evaluate our method, we compile Multi-domain Fictitious Unlearning (MuFU) forget set across five diverse domains and introduce an evaluation stack to quantify the trade-off between knowledge retention and unlearning effectiveness. Experimental results on various LLMs show that NSPU achieves superior unlearning performance, preserves model utility, and enhances user privacy. Additionally, the proposed approach is at least 10x more computationally efficient than standard unlearning approaches. Our findings foster a new direction for privacy-aware machine unlearning that balances data protection and model fidelity.
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