PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?
This work addresses the need for user-specific privacy reasoning in AI, moving beyond population-level analysis, though it is incremental in applying existing theories to a new domain.
The paper tackled the problem of simulating individual users' privacy concerns in response to real-world news by introducing PRA, an AI-agent design that integrates privacy and cognitive theories, and it showed that PRA outperforms baseline agents in privacy concern prediction on Hacker News discussions.
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.