Privacy in LLM-based Recommendation: Recent Advances and Future Directions

2024/06/03 by Sichun Luo, Luo, Sichun, Wei Shao +21 · 1 citation
Computer Science · #Computation and Language (cs.CL) #Cryptography and Data Security #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2406.01363

openalex publication_date 2024/06/03 · openalex created_date 2024/06/07 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems

Related

• Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective
• Preserving Privacy and Utility in LLM-Based Product Recommendations
• Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey
• Beyond Data Privacy: New Privacy Risks for Large Language Models
• LLM-PBE: Assessing Data Privacy in Large Language Models