{
  "annotation_id": "582d1bde-a82c-466b-ad44-46ecf15965c1",
  "date_created": "2026-02-17T05:53:20.212000Z",
  "date_modified": "2026-02-17T05:53:20.212000Z",
  "file_hash": "fe5b82ea401170896422248def98f60ddddcac9b1f0a6650fb4446bcc1cd6fe0",
  "private": false,
  "record": {
    "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\u0027s \"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.",
    "arxiv_id": "2601.09152",
    "authors": [
      "Yiwen Tu",
      "Xuan Liu",
      "Lianhui Qin",
      "Haojian Jin"
    ],
    "categories": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?",
    "url": "https://arxiv.org/abs/2601.09152",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "dfa36783-2d16-444e-bbb6-2ba2e07bc1d5",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}