Privacy in LLM-based Recommendation: Recent Advances and Future Directions
It addresses privacy concerns for users in LLM-enhanced recommendation systems, but is incremental as it is a review paper.
The paper reviews recent advancements in privacy issues in LLM-based recommendation systems, categorizing them into privacy attacks and protection mechanisms, and proposes future directions to address these challenges.
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.