Differentially Private Preference Data Synthesis for Large Language Model Alignment

Preference alignment is a crucial post-training step for large language models (LLMs) to ensure their outputs align with human values. However, post-training on real human preference data raises privacy concerns, as these datasets often contain sensitive user prompts and human judgments. To address this, we propose DPPrefSyn, a novel algorithm for generating differentially private (DP) synthetic preference data to enable privacy-preserving preference alignment. DPPrefSyn is a principled framewor

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ethics.ai (29 May 2026), “Differentially Private Preference Data Synthesis for Large Language Model Alignment,” evidence record 3459, https://ethics.ai/record/3459 (originally published by arXiv).

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