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Contextual Privacy in LLMs

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2026-03-02 03:44:27

raw text

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory ConfAIde : Can LLMs Keep a Secret? Testing Privacy Implications of Language Models Niloofar Mireshghallah 1* , Hyunwoo Kim 2* , Xuhui Zhou 3 , Yulia Tsvetkov 1 , Maarten Sap 3 , Reza Shokri 4 , Yejin Choi 1,2 1 University of Washington 2 Allen Institute for Artificial Intelligence 3 Carnegie Mellon University 4 National University of Singapore * Equal Contribution Image credit: Bing Image Creator Paper arXiv Code Data Abstract The interactive use of large language models (LLMs) in AI assistants (at work, home, etc.) introduces a new set of inference-time privacy risks: LLMs are fed different types of information from multiple sources in their inputs and we expect them to reason about what to share in their outputs, for what purpose and with whom, in a given context. In this work, we draw attention to the highly critical yet ...

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