Privacy Reasoning in Ambiguous Contexts

• URL: http://arxiv.org/abs/2506.12241v1
• Date: Fri, 13 Jun 2025 21:42:22 GMT
• Title: Privacy Reasoning in Ambiguous Contexts
• Authors: Ren Yi, Octavian Suciu, Adria Gascon, Sarah Meiklejohn, Eugene Bagdasarian, Marco Gruteser,
• Abstract summary: We study the ability of language models to reason about appropriate information disclosure.<n>We identify context ambiguity as a crucial barrier for high performance in privacy assessments.
• Score: 4.766316946591749
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We study the ability of language models to reason about appropriate information disclosure - a central aspect of the evolving field of agentic privacy. Whereas previous works have focused on evaluating a model's ability to align with human decisions, we examine the role of ambiguity and missing context on model performance when making information-sharing decisions. We identify context ambiguity as a crucial barrier for high performance in privacy assessments. By designing Camber, a framework for context disambiguation, we show that model-generated decision rationales can reveal ambiguities and that systematically disambiguating context based on these rationales leads to significant accuracy improvements (up to 13.3\% in precision and up to 22.3\% in recall) as well as reductions in prompt sensitivity. Overall, our results indicate that approaches for context disambiguation are a promising way forward to enhance agentic privacy reasoning.

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