{
  "id": 18026,
  "url": "https://arxiv.org/abs/2608.09164v1",
  "title": "CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment",
  "summary": "Aligning large language models (LLMs) with human privacy preferences requires capturing individuals' disclosure boundaries beyond general privacy norms. However, a gap remains in eliciting such nuanced preferences to evaluate alignment in realistic settings. We introduce CIDER, a dataset of 14,850 human annotations from 169 users, forming 1,650 contextual disclosure boundary sets across 60 interpersonal communication scenarios involving information sharing that violates privacy norms. Each bound",
  "authors": "Bingcan Guo, Eryue Xu, Jijie Zhou, Zhiping Zhang, Tianshi Li",
  "category": "research",
  "topics": "safety-alignment,privacy-surveillance,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T06:17:19.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18026",
  "original_url": "https://arxiv.org/abs/2608.09164v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}