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Privacy in LLM-based Recommendation: Recent Advances and Future Directions
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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.
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Cited by 2 Pith papers
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LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
A systematic review that categorizes LLM threats, severity scores, and mitigations across development and operation life cycles and multiple deployment scenarios.
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Preserving Privacy and Utility in LLM-Based Product Recommendations
A hybrid system that filters sensitive purchases out of LLM-based recommendation prompts and generates those recommendations locally nearly matches full-data recommendation quality while keeping most sensitive data of...
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