PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?

Yiwen Tu; Xuan Liu; Lianhui Qin; Haojian Jin

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arXiv · 2601.09152

PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?

Yiwen Tu·Xuan Liu·Lianhui Qin·Haojian Jin

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Abstract

Prior work on LLM-based privacy focuses on norm judgment over synthetic vignettes, rather than how people think about a specific data practice and formulate their opinions. We address this gap by designing PrivacyReasoner, an agent architecture grounded in three key ideas: (1) LLMs can detect subtle privacy cues in natural language and role-play human characteristics; (2) a user's ``privacy mind'' can be reconstructed from their real-world online comment history, distilling experiences, personality, and cultural orientations; and (3) a contextual filter can dynamically activate relevant privacy beliefs based on the contexts in a scenario. We evaluate PrivacyReasoner on real-world privacy discussions from Hacker News, using an LLM-as-a-Judge evaluator calibrated against an established privacy concern taxonomy to quantify reasoning faithfulness. PrivacyReasoner significantly outperforms baselines in predicting individual privacy concerns and generalizes across different domains, such as AI, e-commerce, and healthcare.

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Yiwen Tu, Xuan Liu, Lianhui Qin, Haojian Jin. 2026-01-14. PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?. https://arxiv.org/abs/2601.09152

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