Beyond Individual Concerns: Multi-user Privacy in Large Language Models

In this paper, we explore the nuanced and increasingly relevant issue of Multi-user Privacy (MP) in the context of Large Language Models (LLMs). Addressing the gap in current research, we examine how LLMs can inadvertently compromise the privacy of multiple users, particularly in scenarios involving advanced multimodal capabilities. We highlight the challenges in mitigating these privacy concerns, stemming from the complexities of shared data permissions, varying user perceptions of privacy, and the dynamic nature of LLM interactions. The paper advocates for a collaborative approach, encompassing targeted research, ethical AI development, informed policy-making, and enhanced user awareness, to address these emerging privacy challenges in the realm of LLMs.