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

• URL: http://arxiv.org/abs/2601.09152v1
• Date: Wed, 14 Jan 2026 04:47:06 GMT
• Title: PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?
• Authors: Yiwen Tu, Xuan Liu, Lianhui Qin, Haojian Jin,
• Abstract summary: This paper introduces PRA, an AI-agent design for simulating how individual users form privacy concerns in response to real-world news.<n>Experiments on real-world Hacker News discussions show that PRA outperforms baseline agents in privacy concern prediction.
• Score: 13.499949825312797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces PRA, an AI-agent design for simulating how individual users form privacy concerns in response to real-world news. Moving beyond population-level sentiment analysis, PRA integrates privacy and cognitive theories to simulate user-specific privacy reasoning grounded in personal comment histories and contextual cues. The agent reconstructs each user's "privacy mind", dynamically activates relevant privacy memory through a contextual filter that emulates bounded rationality, and generates synthetic comments reflecting how that user would likely respond to new privacy scenarios. A complementary LLM-as-a-Judge evaluator, calibrated against an established privacy concern taxonomy, quantifies the faithfulness of generated reasoning. Experiments on real-world Hacker News discussions show that \PRA outperforms baseline agents in privacy concern prediction and captures transferable reasoning patterns across domains including AI, e-commerce, and healthcare.

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