Rank-Aware Differentially Private Release of Listwise Preferences for LLM Alignment

Researchers propose a rank-aware exponential mechanism to apply local differential privacy to listwise preference data for LLM alignment, addressing the high sensitivity of full rankings by sampling a fixed-size binary partition that concentrates noise near borderline items.