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.

基于大型语言模型的系统，在用户隐私方面存在问题，需要更多关注人类隐私问题的研究，包括设计范例对用户的行为披露、用户心智模型和隐私控制偏好的影响，以及赋予终端用户对个人数据的拥有权的工具设计，在此基础上构建可用、高效、具有隐私友好性质的系统。本文旨在启动关于人类隐私问题研究的讨论，为基于大型语言模型的系统中的隐私问题制定议程。