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

2026-01-14

AI Summary

This study addresses the limitation of existing research, which predominantly relies on aggregate sentiment analysis and fails to capture the dynamic process by which individuals develop privacy concerns in real-world news contexts. To this end, the authors propose PRA (Privacy-Reasoning Agent), the first agent-based framework that integrates bounded rationality with personalized privacy mental modeling. Leveraging users’ historical comments and contextual cues, PRA employs a cognitive science–inspired memory activation mechanism to dynamically simulate individual reasoning and responses to emerging privacy events. The work also introduces a privacy concern taxonomy, a synthetic comment generation framework, and a calibrated LLM-as-a-Judge evaluation methodology. Evaluated on real Hacker News data, PRA significantly outperforms baseline models in accurately predicting individual privacy concerns and demonstrates strong transferability across domains such as AI, e-commerce, and healthcare.

Technology Category

• Machine Learning: Privacy
• Knowledge Representation and Reasoning: Preferences
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

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.

Problem

• privacy reasoning
• LLM agent
• user-specific privacy concerns
• bounded rationality
• privacy mind

Innovation

• privacy reasoning
• cognitive modeling
• personalized AI agent
• LLM-as-a-Judge
• bounded rationality