{
  "annotation_id": "809e40dd-db13-41d0-aa3b-066c94ba50ec",
  "date_created": "2026-02-17T05:53:07.569000Z",
  "date_modified": "2026-02-17T05:53:07.569000Z",
  "file_hash": "982464c8107101513a0809460770532eae345f258a8eb0ef2126b55c6af39066",
  "private": false,
  "record": {
    "abstract": "Most users agree to online privacy policies without reading or understanding them, even though these documents govern how personal data is collected, shared, and monetized. Privacy policies are typically long, legally complex, and difficult for non-experts to interpret. This paper presents the Smart Privacy Policy Assistant, an LLM-powered system that automatically ingests privacy policies, extracts and categorizes key clauses, assigns human-interpretable risk levels, and generates clear, concise explanations. The system is designed for real-time use through browser extensions or mobile interfaces, surfacing contextual warnings before users disclose sensitive information or grant risky permissions. We describe the end-to-end pipeline, including policy ingestion, clause categorization, risk scoring, and explanation generation, and propose an evaluation framework based on clause-level accuracy, policy-level risk agreement, and user comprehension.",
    "arxiv_id": "2601.06357",
    "authors": [
      "Sriharshini Kalvakuntla",
      "Luoxi Tang",
      "Yuqiao Meng",
      "Zhaohan Xi"
    ],
    "categories": [
      "cs.CR",
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Smart Privacy Policy Assistant: An LLM-Powered System for Transparent and Actionable Privacy Notices",
    "url": "https://arxiv.org/abs/2601.06357",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e332b847-64e3-4dc2-85f6-31c3a9016817",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}