DP-ES: Differentially Private Evolution Strategies for Prompt Optimization
Token-level differentially private (DP) prompt optimization methods such as DP-OPT can become unstable under tight privacy budgets: on GSM8K, DP-OPT obtains $49.5\pm28.5\%$ across 30 runs, and a logged search trajectory reveals prompt-template drift and noise-sensitive irreversible choices. We diagnose these as structural consequences of greedy token-by-token construction over privately aggregated counts. We then propose DP-ES (Differentially Private Evolution Strategies), a structurally cleaner alternative that maintains a population of full prompts, mutates them via LLM calls that never access the private dataset, and spends privacy only on sampled-Gaussian evaluation; deterministic or ...
| Authors | Ziniu Liu, Aiping Li, Yue Han et al. |
|---|---|
| Primary category | cs.CR |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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