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 framework grounded in the Bradley-Terry preference model and the intrinsic geometric structure of pairwise human preference data. It first learns an underlying preference model from private data with forma...
| Authors | Fengyu Gao, Jing Yang |
|---|---|
| Primary category | cs.CR |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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