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Protecting Private Code in IDE Autocomplete using Differential Privacy

This paper demonstrates that training a Kotlin code completion model with Differential Privacy effectively mitigates Membership Inference Attacks while maintaining high utility comparable to non-private models, even when trained on significantly less data.

Original authors: Evgeny Grigorenko, David Stanojević, David Ilić, Egor Bogomolov, Kostadin Cvejoski

Published 2026-02-02

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CC BY 4.0

Original authors: Evgeny Grigorenko, David Stanojević, David Ilić, Egor Bogomolov, Kostadin Cvejoski

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Problem: The "Over-Attentive" Chef

Imagine you hire a master chef (an AI model) to help you cook. You want the chef to learn from your family's secret recipes so they can suggest the perfect ingredients for your next meal. This is what modern coding tools do: they learn from the code developers write to suggest the next lines of code.

However, there is a risk. If the chef is too good at memorizing, they might accidentally spit out your family's secret recipe word-for-word when asked to cook something completely different. Worse, a nosy neighbor (a hacker) could ask the chef specific questions to figure out, "Did you learn this specific recipe from my family's cookbook, or did you make it up?" If the chef can answer "Yes, definitely," the neighbor knows your secret data was used to train them.

In the world of coding, this is called Memorization and Membership Inference Attacks. The paper shows that standard AI models are like these over-attentive chefs: they memorize private code snippets too well, creating a security leak.

The Solution: The "Foggy Mirror" (Differential Privacy)

To fix this, the researchers used a technique called Differential Privacy (DP).

Think of training an AI like polishing a mirror to see a reflection clearly.

Without DP: You polish the mirror until it perfectly reflects every tiny detail of the object in front of it. If you put a specific, unique object (a private code snippet) in front of it, the mirror reflects it perfectly. A hacker can look at the reflection and say, "That was definitely the object I put there!"
With DP: The researchers add a special "foggy spray" to the mirror while polishing it. This spray adds a little bit of static noise to the reflection. Now, the mirror still shows the general shape of the object (the code still works well), but the tiny, unique details are blurred out.

Because of this "fog," even if a hacker asks, "Is this specific object in the mirror?" the mirror's answer is so blurry that the hacker can't tell. They are forced to guess, like flipping a coin.

How They Did It

The team took a powerful AI model (called Mellum) that was already good at writing code. Instead of teaching it all over again, they used a smart shortcut called LoRA (Low-Rank Adaptation).

The Analogy: Imagine the AI is a giant library with billions of books. Instead of rewriting every single book to learn new secrets, they just added a small, special notebook to the librarian's desk. They only trained this small notebook.
Why this matters: Because they only trained a small notebook, they could add the "foggy spray" (noise) much more efficiently without ruining the librarian's ability to help.

They trained this model on 80,000 private code snippets from their own company's internal files, while a "control group" model was trained on 8 million snippets without the privacy protection.

The Results: Safe and Still Smart

The researchers tested two things: Privacy (Can a hacker guess what was in the training data?) and Utility (Does the AI still write good code?).

The Privacy Test (The Hacker's Guess):
- The non-private model was a terrible secret keeper. When hackers tried to guess if a specific code snippet was in the training data, the model gave them a 90% success rate. It was practically shouting, "Yes, I know this!"
- The privacy-protected model was a master of disguise. The hackers' success rate dropped to 60%. This is barely better than a random guess (50%). The "fog" worked; the model couldn't tell the difference between data it saw and data it didn't.
The Utility Test (The Code Quality):
- Usually, adding "fog" makes things worse. You'd expect the privacy-protected model to write bad code.
- Surprisingly, it didn't. The privacy-protected model wrote code almost as well as the non-private one, even though it was trained on 100 times less data (80k vs 8 million).
- The "fog" actually acted like a filter, helping the model learn the general rules of coding without getting distracted by specific, weird details.

The Bottom Line

The paper proves that you can build an AI coding assistant that respects your privacy without sacrificing its smarts. By using a mathematical "fog" (Differential Privacy) and a smart training method (LoRA), they created a model that:

Refuses to leak your private code snippets.
Stops hackers from guessing if your code was used to train it.
Still writes great code, even though it learned from a much smaller dataset.

In short, they found a way to teach the AI to be helpful without teaching it to be a gossip.

Technical Summary: Protecting Private Code in IDE Autocomplete using Differential Privacy

Problem Statement

Modern Integrated Development Environments (IDEs) increasingly rely on Large Language Models (LLMs) for code completion features. While these models enhance developer productivity, training them on user-written code introduces significant privacy risks. LLMs are known to exhibit "memorization," where they can verbatim reproduce sensitive training data, such as secrets, passwords, or proprietary algorithms, given the right prompt. Recent studies have demonstrated that malicious actors can exploit this via Membership Inference Attacks (MIAs) to determine if specific sensitive code snippets were part of a model's training set.

Current defenses are often insufficient, and while Differential Privacy (DP) offers a mathematical framework for privacy, its practical application in deep learning is challenging. It typically requires computing per-sample gradients (computationally expensive) and adding noise that degrades model utility. Furthermore, existing DP research has largely focused on general language models, leaving a critical gap in the specialized domain of code generation, where no code completion LLM with formal DP guarantees had been developed prior to this work.

Methodology

The authors propose a framework to fine-tune a large pre-trained code completion model using formal Differential Privacy guarantees to protect internal, sensitive code repositories.

Base Model: The study utilizes Mellum, a 4-billion-parameter, LLaMA-style Transformer pre-trained on 4 trillion tokens of public code, with a specialization in Kotlin. The model supports Fill-in-the-Middle (FIM), allowing it to generate code based on both prefix and suffix contexts (e.g., <PRE>prefix<SUF>suffix<MID>).
Fine-Tuning Strategy: Instead of updating all model weights, the authors employ Low-Rank Adaptation (LoRA). This parameter-efficient technique freezes pre-trained parameters and injects trainable low-rank matrices. This reduces the dimensionality of gradients, making the addition of DP noise more efficient and helping maintain model utility.
Privacy Mechanism: The training process utilizes Differentially Private Stochastic Gradient Descent (DP-SGD). The workflow involves:
1. Per-Example Gradient Computation: Calculating gradients for each sample in a batch.
2. Gradient Clipping: Limiting the influence of any single data point by clipping the L2 norm of gradients to a threshold ( $C=0.5$ ).
3. Noise Addition: Adding Gaussian noise (with a noise multiplier $\sigma=0.2746$ ) to the averaged clipped gradients before updating the model.
4. Privacy Accounting: Tracking the cumulative privacy cost to ensure a final $(\epsilon, \delta)$ guarantee.
Experimental Setup:
- Baseline: A non-private model (Mellum Int. Base) fine-tuned on 8 million internal Kotlin instances.
- Private Model: A differentially private model (Mellum Int. DP) fine-tuned on only 80,000 internal instances (due to early stopping based on the privacy budget).
- Hyperparameters: The optimal configuration was found to be a LoRA rank of $r=8$ and a privacy budget of $\epsilon \approx 30$ .

Key Contributions

First DP-Protected Code LLM: To the authors' knowledge, this is the first code completion LLM specifically designed for safe IDE integration using formal DP guarantees.
Practical Privacy-Utility Trade-off: The study demonstrates that DP can be applied to large-scale code models with a minimal impact on performance, even when trained on significantly less data (100x less) than the non-private baseline.
Robust Defense Against MIAs: The work provides empirical evidence that DP effectively neutralizes Membership Inference Attacks in the context of code generation.

Results

The evaluation focused on two dimensions: Utility (code generation quality) and Privacy (resistance to MIAs).

Privacy Performance (MIA Resistance):
- The non-private baseline was highly vulnerable to MIAs, achieving an Area Under the Curve (AUC) of 0.901, indicating an attacker could reliably distinguish training data from unseen data.
- The DP-trained model reduced the attack success rate to an AUC of 0.606, which is close to a random guess (0.5). This confirms that the model successfully obfuscates the influence of individual training examples.
Utility Performance:
- Despite the privacy constraints and the use of 100x less training data, the DP model maintained high utility.
- On an internal Kotlin benchmark, the DP model achieved scores comparable to the non-private baseline:
  - ChrF++: 74.78 (DP) vs. 75.26 (Non-DP).
  - LLM-as-judge: 0.730 (DP) vs. 0.753 (Non-DP).
  - LM Score: 0.613 (DP) vs. 0.643 (Non-DP).
- The DP model outperformed the original pre-trained Mellum SFT model across all metrics, suggesting DP-SGD may act as a strong regularizer that aids generalization.
Training Dynamics: As expected, the DP model exhibited a slightly higher validation loss due to the noise injection, but the privacy budget ( $\epsilon \approx 30$ ) was spent gradually over the training epoch.

Significance and Claims

The paper claims that Differential Privacy is a practical and effective solution for building trustworthy, AI-powered IDE features. The authors emphasize that while their final privacy budget ( $\epsilon \approx 30$ ) is higher than the strict thresholds often targeted in theoretical work ( $\epsilon \le 10$ ), it provides powerful protection in a real-world IDE scenario. The study concludes that even with "looser" privacy budgets, DP successfully hinders Membership Inference Attacks, reducing the attacker's advantage to that of a random guess without significantly compromising the model's ability to assist developers.

The work highlights that the trade-off between privacy and utility is highly favorable in this domain, suggesting that secure, privacy-preserving developer tools can be built without sacrificing the quality of code suggestions. Future work is proposed to explore stricter privacy budgets and training on larger datasets to further refine this balance.