Privacy in LLM-based Recommendation: Recent Advances and Future Directions
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 41 of its 76 references that are in the map. First posted on arXiv 3 Jun 2024.
Builds on
41 of 76 references.
References · 41
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29 more
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