The emergence of large language models (LLMs), and their increased use in user-facing systems, has led to substantial privacy concerns. To date, research on these privacy concerns has been model-centered: exploring how LLMs lead to privacy risks like memorization, or can be used to infer personal characteristics about people from their content. We argue that there is a need for more research focusing on the human aspect of these privacy issues: e.g., research on how design paradigms for LLMs affect users' disclosure behaviors, users' mental models and preferences for privacy controls, and the design of tools, systems, and artifacts that empower end-users to reclaim ownership over their personal data. To build usable, efficient, and privacy-friendly systems powered by these models with imperfect privacy properties, our goal is to initiate discussions to outline an agenda for conducting human-centered research on privacy issues in LLM-powered systems. This Special Interest Group (SIG) aims to bring together researchers with backgrounds in usable security and privacy, human-AI collaboration, NLP, or any other related domains to share their perspectives and experiences on this problem, to help our community establish a collective understanding of the challenges, research opportunities, research methods, and strategies to collaborate with researchers outside of HCI.

翻译：大型语言模型的出现及其在面向用户系统中的广泛应用引发了显著的隐私问题。迄今为止，关于这些隐私问题的研究主要聚焦于模型层面：探讨LLMs如何导致记忆化等隐私风险，或如何被用于从用户内容中推断个人特征。我们主张，需要更多研究关注这些隐私问题中的人本维度——例如：LLMs的设计范式如何影响用户的披露行为、用户对隐私控制的心理模型与偏好，以及如何设计能赋予终端用户重新掌握个人数据主权的工具、系统和制品。为了构建基于这些存在不完美隐私属性模型的可用的、高效且隐私友好的系统，我们旨在发起共同讨论，为在LLM驱动系统中开展人本隐私研究制定议程。本研究兴趣小组旨在汇聚来自可用安全与隐私、人机协同、自然语言处理及相关领域的研究人员，分享对此问题的视角与经验，帮助社区形成对挑战、研究机会、研究方法以及与人类-计算机交互领域之外研究者协作策略的共识。