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

This article from ArXiv discusses the development of differentially private code language models, focusing on privacy-preserving code generation. The research likely explores methods to generate code while minimizing the risk of revealing sensitive information from the training data. The use of differential privacy suggests a rigorous approach to protecting individual data points.

• •Focuses on privacy-preserving code generation.
• •Employs differentially private code language models.
• •Aims to protect sensitive information in training data.