Enterprise AI Analysis

Towards Privacy-Preserving Code Generation: Differentially Private Code Language Models

By Melih Catal, Pooja Rani, and Harald Gall

This study investigates the application of Differential Privacy (DP) to Code Language Models (CodeLLMs) to mitigate memorization risks, which can lead to privacy breaches and intellectual property violations. Despite the promising capabilities of CodeLLMs in generating code, their tendency to inadvertently reproduce training data limits their deployment in sensitive domains. This research aims to systematically evaluate DP's effectiveness in reducing memorization while preserving model utility, training efficiency, and energy consumption.

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Executive Impact & Key Findings

Differential Privacy (DP) significantly reduces memorization in CodeLLMs across all snippet types, particularly for frequent and simple code. This mitigation is achieved without compromising code generation capabilities—functional correctness is preserved and sometimes enhanced—and without significant increases in training time or energy consumption. DP presents a practical and sustainable solution for privacy-preserving CodeLLM development.

0% Memorization Reduction for Licenses

0% Memorization Reduction for Imports

0x Functional Correctness Preserved/Enhanced

0% Training Overhead Increase

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

DP Fundamentals

Memorization Insights

Performance vs. Privacy

Efficiency & Sustainability

Privacy Evaluation Pipeline

Business Imperative

Limitations & Future Work

Understanding Differential Privacy for CodeLLMs

What is Differential Privacy?

Differential Privacy (DP) is a mathematical framework that adds calibrated noise to data during model training to protect individual data points. This ensures that the inclusion or exclusion of any single training sample does not significantly affect the model's output, thereby preventing inference or extraction of sensitive information.

DP in CodeLLMs: Balancing Privacy and Utility

In the context of CodeLLMs, DP mitigates memorization risks by adding noise to gradients during fine-tuning. This study specifically applies DP to protect each individual code snippet. Key hyperparameters include:

Clipping Norm (C): Limits the maximum influence of any single training sample on model updates.
Noise Multiplier (σ): Controls the amount of Gaussian noise added to gradients.
Privacy Budget (ε, δ): Quantifies the overall privacy guarantee; lower ε means stronger privacy. Our study tested ε values of 0.1, 1, and 10.
Batch Size (L): Affects privacy accounting and model convergence; larger batches generally reduce gradient noise but weaken privacy guarantees due to reduced subsampling amplification.

Our findings demonstrate that DP can effectively reduce memorization in CodeLLMs, offering a robust defense mechanism against privacy breaches without significantly compromising model utility.

Characterizing Memorization in CodeLLMs

Types of Memorized Snippets

CodeLLMs can memorize a wide range of snippets. Our taxonomy categorized them into high-level types: License (0.7%), Documentation (23.6%), Code (57.2%), and Data Structures (18.4%). The 'Code' category was further refined into Control Flow (25.2%), Import Statements (17.9%), Testing Code (23.5%), Expressions (15.3%), Definitions (13.4%), and Declarations (4.5%).

Licenses were found to be the most frequently memorized snippet type, likely due to their common presence in code repositories. However, memorization extends to all other snippet types, including unique code, highlighting broad privacy risks.

Factors Driving Memorization

We found that frequency has a significant impact; more frequent snippets are more likely to be memorized (OR=3.17 for snippet types, OR=8.91 for code types). Complexity also plays a role, with simpler, more compressible snippets being more prone to memorization (OR=0.60 for snippet types, OR=0.38 for code types). This suggests models prioritize easier-to-learn and reproduce patterns.

Evolution of Memorization

Memorization behavior consolidates over fine-tuning, meaning snippets are initially memorized or forgotten, but a stable set of memorized snippets emerges as training progresses. This dynamic process underscores the importance of addressing memorization during fine-tuning.

70% Memorization Reduction for Licenses

Even a slight amount of Differential Privacy (ε = 10) can reduce the memorization rate of license snippets by approximately 70%, significantly enhancing privacy for a highly vulnerable snippet type.

DP Impact on Code Generation Capabilities

Preserving Functional Correctness

Our evaluation using the HumanEval (general code generation) and SPE-NC (fine-tuning dataset specific) benchmarks showed that DP does not significantly degrade the overall code generation performance of CodeLLMs. Models fine-tuned with DP achieved comparable, and in some cases even slightly better, pass@k scores (for k=1, 5, and 10) compared to non-DP counterparts (all p > 0.05). This suggests that DP can be applied without compromising the model's ability to generate valid and functional code.

Perplexity Trade-off

While functional correctness was maintained, models fine-tuned with DP exhibited slightly higher perplexity scores on the test set, with lower epsilon values corresponding to higher perplexity. This indicates that DP introduces some noise that affects the model's ability to predict text, but this does not translate into a degradation of code generation performance as measured by functional correctness. Evaluating with multiple metrics is crucial as they capture different aspects of model performance.

Privacy Evaluation Pipeline for CodeLLMs

Model Preparation (Fine-tuning with/without DP)

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Memorization Detection & Categorization (Snippet & Match Types)

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Memorization Filtering (Exclude Pre-training Memorization)

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Privacy Evaluation (Compare Memorized Records)

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Utility Evaluation (Overall & Fine-Tuned Dataset Performance)

DP's Impact on Training Efficiency and Energy Consumption

Negligible Overhead

One of the critical findings is that DP does not significantly affect training time or energy usage. While a slight increase in energy consumption and training time was observed compared to the non-DP baseline, statistical analysis confirmed these differences were insignificant (p > 0.05 for energy consumption, power usage, average training time per epoch, and average throughput).

Practical Implications

This demonstrates that DP can be implemented in CodeLLMs without incurring meaningful overhead in real-world training pipelines. Privacy enhancements can be achieved without compromising efficiency or sustainability, making DP a practical choice for developers and organizations concerned with responsible AI development.

The Business Imperative: DP in Sensitive Code Domains

Scenario: A leading financial institution wants to leverage CodeLLMs for automated code generation, but strict regulatory compliance and intellectual property concerns prohibit using models trained on sensitive internal codebases due to memorization risks. Publicly available models are insufficient for their specialized needs.

Solution: By applying Differential Privacy during fine-tuning of CodeLLMs on their proprietary code, the institution can mitigate the risk of private information leakage. DP ensures that unique or sensitive internal code snippets are not memorized and reproduced, allowing the model to learn useful patterns without exposing confidential data. This enables the secure deployment of powerful CodeLLMs for tasks like compliance automation, secure API development, and internal tool creation, enhancing developer productivity while maintaining stringent privacy and IP controls.

Outcome: The institution successfully deployed DP-enabled CodeLLMs, reducing development cycles by 20% for new features and achieving 100% compliance with data privacy regulations for generated code. This innovative approach transformed their secure software development lifecycle.

Study Limitations and Future Research Directions

Current Limitations

This study's analysis is based on a specific set of CodeLLMs and fine-tuning datasets, which may not be fully representative of all models in practice. The data extraction attack employed may not capture all instances of memorization, particularly fuzzy memorization. Furthermore, the evaluation metrics used, such as functional correctness and perplexity, may not fully capture the nuanced utility of CodeLLMs in real-world scenarios. Finally, the specific DP algorithms and parameters used might not be optimal for all scenarios.

Future Research Avenues

Future work could explore a broader range of models and datasets, develop more sophisticated attack methods to quantify memorization more accurately, and consider additional metrics that better reflect practical utility. Research could also investigate alternative DP techniques and configurations. Furthermore, integrating DP with other privacy-preserving techniques like federated learning or secure multi-party computation could offer enhanced security. Exploring DP's impact on other CodeLLM aspects, such as code comprehension and debugging, and developing category-aware DP mechanisms that tailor privacy guarantees based on snippet characteristics, are promising directions.

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Your Implementation Roadmap

A phased approach to integrating privacy-preserving CodeLLMs into your enterprise.

Discovery & Strategy

Assess current code generation workflows, identify high-impact areas for CodeLLM integration, and define privacy requirements. Select appropriate CodeLLM architectures and DP configurations.

Pilot & Fine-tuning

Develop a proof-of-concept, fine-tune CodeLLMs with DP on a representative subset of proprietary data, and establish initial privacy-utility trade-offs. Evaluate performance against internal benchmarks.

Integration & Deployment

Integrate DP-enabled CodeLLMs into existing IDEs and CI/CD pipelines. Deploy models in a secure, controlled environment, monitoring for performance and privacy compliance.

Monitoring & Optimization

Continuously monitor model outputs for memorization risks and utility. Iterate on DP parameters and model configurations for ongoing optimization and adaptation to evolving needs.

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