Human-Centered Privacy Research in the Age of Large Language Models
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914664636481536 |
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| author | Li, Tianshi Das, Sauvik Lee, Hao-Ping Wang, Dakuo Yao, Bingsheng Zhang, Zhiping |
| author_facet | Li, Tianshi Das, Sauvik Lee, Hao-Ping Wang, Dakuo Yao, Bingsheng Zhang, Zhiping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01994 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human-Centered Privacy Research in the Age of Large Language Models Li, Tianshi Das, Sauvik Lee, Hao-Ping Wang, Dakuo Yao, Bingsheng Zhang, Zhiping Human-Computer Interaction Artificial Intelligence Cryptography and Security 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. |
| title | Human-Centered Privacy Research in the Age of Large Language Models |
| topic | Human-Computer Interaction Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2402.01994 |