CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment
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
Record details
Published: 10 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Privacy · Transparency
Retrieved: 11 August 2026
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ethics.ai (10 August 2026), “CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment,” evidence record 18026, https://ethics.ai/record/18026 (originally published by arXiv).
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