Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance. However, while most of the existing works have focused on improving the model performance, the privacy issue has only received comparatively less attention. In this paper, we review recent advancements in privacy within LLM-based recommendation, categorizing them into privacy attacks and protection mechanisms. Additionally, we highlight several challenges and propose future directions for the community to address these critical problems.

翻译：当前，大语言模型（LLMs）已与传统推荐模型相结合，以提升推荐性能。然而，尽管现有研究大多聚焦于改进模型性能，隐私问题所受到的关注相对较少。本文回顾了基于LLM的推荐系统中隐私保护的最新进展，将其归类为隐私攻击与保护机制。此外，我们指出了若干挑战，并为学界提出了应对这些关键问题的未来研究方向。