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

Researchers introduce PrivacyReasoner, an LLM-based agent architecture that reconstructs individual privacy perspectives from online comment history to predict how specific people would perceive data practices. The system outperforms baseline models in predicting privacy concerns across AI, e-commerce, and healthcare domains by contextually activating relevant privacy beliefs.

PrivacyReasoner addresses a meaningful gap in privacy research by shifting focus from abstract norm judgments to personalized privacy reasoning. Traditional LLM privacy work evaluates how models judge synthetic scenarios against general ethical standards, but fails to capture how individual users with distinct experiences and cultural backgrounds actually think about real data practices. This research demonstrates that LLMs can extract latent privacy profiles from natural language comment histories, enabling context-aware privacy prediction that reflects genuine human heterogeneity in privacy preferences.

The architecture's three-part design reflects sophisticated understanding of privacy psychology. By detecting subtle linguistic cues, role-playing individual characteristics, and dynamically filtering beliefs based on scenario context, PrivacyReasoner moves beyond one-size-fits-all privacy models. The evaluation on real Hacker News discussions and calibration against established privacy taxonomies provides empirical grounding rather than speculative claims.

For the technology industry, this work has immediate implications for privacy-preserving AI development, personalized privacy controls, and user-centric data governance. Organizations building AI systems could incorporate such reasoning to better align with heterogeneous user privacy expectations. The cross-domain generalization across AI, e-commerce, and healthcare suggests the approach isn't limited to niche applications. However, the research also raises important questions about privacy inference itself—reconstructing detailed privacy profiles from online data creates new privacy risks that require careful consideration before deployment.

• →PrivacyReasoner reconstructs individual privacy perspectives from online comment history to enable personalized privacy prediction
• →The system significantly outperforms baselines by contextually activating relevant privacy beliefs based on scenario-specific details
• →Architecture demonstrates LLMs can detect subtle privacy cues and role-play human characteristics for privacy reasoning
• →Cross-domain generalization across AI, e-commerce, and healthcare domains indicates broad applicability of the approach
• →Privacy inference itself creates new data risks that must be addressed before real-world deployment of such systems