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.
@article{arxiv.2601.09152,
title = {PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?},
author = {Yiwen Tu and Xuan Liu and Lianhui Qin and Haojian Jin},
journal= {arXiv preprint arXiv:2601.09152},
year = {2026}
}