Large language models (LLMs) have presented outstanding performance in code generation and completion. However, fine-tuning these models on private datasets can raise privacy and proprietary concerns, such as the leakage of sensitive personal information. Differentially private (DP) code generation provides theoretical guarantees for protecting sensitive code by generating synthetic datasets that preserve statistical properties while reducing privacy leakage concerns. However, DP code generation faces significant challenges due to the strict syntactic dependencies and the privacy-utility trade-off. We propose PrivCode, the first DP synthesizer specifically designed for code datasets. It incorporates a two-stage framework to improve both privacy and utility. In the first stage, termed "privacy-sanitizing", PrivCode generates DP-compliant synthetic code by training models using DP-SGD while introducing syntactic information to preserve code structure. The second stage, termed "utility-boosting", fine-tunes a larger pre-trained LLM on the synthetic privacy-free code to mitigate the utility loss caused by DP, enhancing the utility of the generated code. Extensive experiments on four LLMs show that PrivCode generates higher-utility code across various testing tasks under four benchmarks. The experiments also confirm its ability to protect sensitive data under varying privacy budgets. We provide the replication package at the anonymous link.

翻译：大型语言模型（LLMs）在代码生成与补全任务中展现出卓越性能。然而，在私有数据集上对这些模型进行微调可能引发隐私与所有权问题，例如敏感个人信息的泄露。差分隐私（DP）代码生成为保护敏感代码提供了理论保障，其通过生成在保留统计特性的同时降低隐私泄露风险的合成数据集来实现。然而，由于严格的语法依赖性与隐私-效用的权衡，DP代码生成面临显著挑战。我们提出PrivCode，这是首个专为代码数据集设计的差分隐私合成器。它采用两阶段框架以同时提升隐私性与效用性。在第一阶段（称为“隐私净化”），PrivCode通过使用DP-SGD训练模型并引入语法信息以保持代码结构，从而生成符合差分隐私要求的合成代码。第二阶段（称为“效用增强”）则在合成且无隐私风险的代码上对更大的预训练LLM进行微调，以缓解差分隐私带来的效用损失，从而提升生成代码的实用性。在四个LLM上进行的广泛实验表明，PrivCode在四种基准测试下的各类任务中均能生成更高实用性的代码。实验也证实了其在不同的隐私预算下保护敏感数据的能力。我们已在匿名链接中提供了复现资源包。