[Paper] Towards Privacy-Preserving Code Generation: Differentially Private Code Language Models

Source: arXiv - 2512.11482v1

Overview

Large language models that specialize in code (CodeLLMs) can write snippets, docs, and tests with impressive fluency, but they also risk leaking proprietary or private code they have memorized from training data. This paper presents the first systematic study of applying Differential Privacy (DP) to CodeLLMs, showing that privacy‑preserving training can dramatically cut memorization while keeping the models useful for developers.

Key Contributions

• First comprehensive DP evaluation for code generation models – establishes a benchmark for privacy‑aware CodeLLMs.
• Empirical analysis of memorization drivers during fine‑tuning, pinpointing which snippet types are most vulnerable.
• Demonstration that DP reduces memorization across all tested snippet categories, with the greatest gains on the most leak‑prone code.
• Evidence that DP only modestly raises perplexity and can even improve downstream generation quality on certain tasks.
• Performance‑aware study showing DP adds negligible overhead to training time and energy consumption, making it practical for real‑world pipelines.

Methodology

1. Model & Data – The authors fine‑tuned a state‑of‑the‑art CodeLLM on a mixed corpus of publicly available code snippets, documentation, and test cases.
2. Memorization Probe – They crafted a suite of “memorization probes” that query the model for exact reproductions of training snippets (e.g., exact function bodies, license headers).
3. Differential Privacy Integration – DP‑SGD (stochastic gradient descent with per‑example gradient clipping and calibrated Gaussian noise) was applied during fine‑tuning. Different privacy budgets (ε values) were explored to trade off privacy vs. utility.
4. Evaluation Metrics –
   • Memorization rate (percentage of probes that return an exact training snippet).
   • Perplexity on a held‑out code validation set (standard language‑model quality metric).
   • Generation quality measured by functional correctness of generated code (e.g., passing unit tests).
   • Training efficiency (wall‑clock time, GPU energy draw).

All steps were implemented with open‑source tooling, and the experiments were repeated across multiple random seeds to ensure robustness.

Results & Findings

Key takeaways

• DP cuts memorization by 80‑85 % even with a tight privacy budget (ε = 1.0).
• The most leak‑prone snippet types (license headers, small utility functions) see the biggest relative drop.
• Model utility is largely preserved; perplexity rises only marginally, and functional correctness of generated code stays the same or improves slightly (likely due to regularization effects of DP noise).
• Training overhead is minimal, confirming that DP can be added to existing pipelines without major cost.

Practical Implications

• Enterprise code assistants can now be trained on internal repositories without exposing proprietary logic, satisfying legal and compliance teams.
• Open‑source model providers can offer “privacy‑guaranteed” variants, attracting customers in regulated sectors (finance, healthcare, defense).
• Developers benefit from code suggestions that are less likely to echo exact snippets from confidential codebases, reducing the risk of accidental IP leakage.
• CI/CD pipelines can incorporate DP‑fine‑tuned CodeLLMs for automated test generation or documentation, knowing the model respects data privacy constraints.
• The modest performance impact means existing tooling (e.g., GitHub Copilot, Tabnine) could adopt DP with only a small engineering effort.

Limitations & Future Work

• Privacy budget selection: The study explores a limited set of ε values; real‑world deployments may need tighter guarantees, which could affect utility more noticeably.
• Scope of memorization probes: While diverse, the probe set focuses on relatively short snippets; longer, more complex code patterns may behave differently.
• Model size: Experiments were run on a mid‑scale CodeLLM; scaling DP to the largest models (e.g., 70B‑parameter) may introduce new challenges in gradient clipping and noise calibration.
• Cross‑language generalization: The work concentrates on a single programming language (Python); extending to multi‑language corpora remains an open question.
• User‑level privacy: The current DP formulation protects individual training examples but does not address higher‑level privacy concerns (e.g., entire projects or developer identities). Future research could explore hierarchical DP or hybrid privacy mechanisms.

Authors

• Melih Catal
• Pooja Rani
• Harald C. Gall

Paper Information

• arXiv ID: 2512.11482v1
• Categories: cs.SE, cs.AI, cs.CR
• Published: December 12, 2025
• PDF: Download PDF