Large language models (LLMs) have emerged as powerful tools for tackling complex tasks across diverse domains, but they also raise privacy concerns when fine-tuned on sensitive data due to potential memorization. While differential privacy (DP) offers a promising solution by ensuring models are `almost indistinguishable' with or without any particular privacy unit, current evaluations on LLMs mostly treat each example (text record) as the privacy unit. This leads to uneven user privacy guarantees when contributions per user vary. We therefore study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users. We present a systematic evaluation of user-level DP for LLM fine-tuning on natural language generation tasks. Focusing on two mechanisms for achieving user-level DP guarantees, Group Privacy and User-wise DP-SGD, we investigate design choices like data selection strategies and parameter tuning for the best privacy-utility tradeoff.

翻译：大型语言模型（LLMs）已成为解决跨领域复杂任务的强大工具，但由于潜在的记忆效应，在敏感数据上进行微调时也引发了隐私担忧。虽然差分隐私（DP）通过确保模型在包含或排除任何特定隐私单元时“几乎无法区分”，提供了一种有前景的解决方案，但目前对LLMs的评估大多将每个样本（文本记录）视为隐私单元。当每个用户的贡献度不同时，这会导致用户隐私保护水平不均。因此，我们研究用户级DP，其动机在于需要确保跨用户统一隐私保护的应用场景。我们对自然语言生成任务中LLM微调的用户级DP进行了系统评估。聚焦于实现用户级DP保证的两种机制——群组隐私和用户级DP-SGD，我们研究了数据选择策略和参数调优等设计选择，以寻求最佳的隐私-效用权衡。