Protecting Private Code in IDE Autocomplete using Differential Privacy

Privacy attacks on code autocomplete are a real threat: The paper demonstrates that without privacy protections, malicious actors can successfully identify whether specific code snippets were used to train IDE autocomplete models, with attacks achieving 90.1% accuracy (AUC of 0.901). This means private or proprietary code could potentially be extracted from these models.

Differential Privacy significantly reduces privacy risks with minimal performance loss: When differential privacy was applied to the code completion model, it reduced the success rate of membership inference attacks to near-random guessing (AUC dropped to 0.606), while maintaining comparable model performance—even when trained on 100 times less data than the non-private version.

Privacy-preserving AI for developer tools is technically feasible: The research shows that protecting sensitive developer code doesn't require sacrificing the quality of autocomplete features, suggesting that companies can implement privacy protections in IDE tools without degrading the user experience developers expect.

Differential privacy offers a "strong defense" against known attacks: The paper specifically identifies differential privacy as a robust mechanism against membership inference attacks, which are a documented method for extracting training data from machine learning models—providing a concrete technical solution for a known vulnerability.