arXiv 2402.019942024-02-03cs.HCcs.AIcs.CR

大型语言模型时代以人为本的隐私研究

Human-Centered Privacy Research in the Age of Large Language Models

Northeastern University（东北大学）；Carnegie Mellon University（卡内基梅隆大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）

Tianshi Li, Sauvik Das, Hao-Ping Lee, Dakuo Wang, Bingsheng Yao, Zhiping Zhang

备注：: 4 pages, CHI EA'24

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AI总结：

针对大型语言模型引发的隐私问题，本文主张从以模型为中心转向以人为本的研究，聚焦用户披露行为、隐私控制设计及数据所有权，并倡议建立研究议程和跨领域合作。

摘要：

大型语言模型（LLM）的出现及其在面向用户系统中的日益广泛使用，引发了重大的隐私担忧。迄今为止，关于这些隐私担忧的研究是以模型为中心的：探讨LLM如何导致诸如记忆化之类的隐私风险，或者如何被用来从人们的内容中推断其个人特征。我们认为，有必要开展更多关注这些隐私问题中人类层面的研究：例如，研究LLM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和偏好，以及设计能够赋予最终用户重新掌控其个人数据所有权的工具、系统和工件。为了构建由这些具有不完美隐私属性的模型驱动的可用、高效且隐私友好的系统，我们的目标是发起讨论，以勾勒出在LLM驱动的系统中开展以人为本的隐私研究议程。本特别兴趣小组（SIG）旨在汇集具有可用安全与隐私、人机协作、自然语言处理（NLP）或任何其他相关领域背景的研究人员，分享他们对这一问题的观点和经验，帮助我们的社区就挑战、研究机会、研究方法以及与HCI之外的研究人员合作的策略建立集体理解。

英文摘要：

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.

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