The potential of transformer-based LLMs risks being hindered by privacy
concerns due to their reliance on extensive datasets, possibly including
sensitive information. Regulatory measures like GDPR and CCPA call for using
robust auditing tools to address potential privacy issues, with Membership
Inference Attacks (MIA) being the primary method for assessing LLMs' privacy
risks. Differently from traditional MIA approaches, often requiring
computationally intensive training of additional models, this paper introduces
an efficient methodology that generates \textit{noisy neighbors} for a target
sample by adding stochastic noise in the embedding space, requiring operating
the target model in inference mode only. Our findings demonstrate that this
approach closely matches the effectiveness of employing shadow models, showing
its usability in practical privacy auditing scenarios.

该研究论文介绍了一种高效的方法，通过在嵌入空间中添加随机噪声，在推理模式下操作目标模型，生成目标样本的 “噪声邻居”，以评估 LLMs 的隐私风险。研究结果表明，该方法与使用影子模型的效果相近，展示了它在实际隐私审计场景中的可用性。