Opacus
Opacus is a high-speed library for training PyTorch models with differential privacy (DP). It provides tools to compute per-sample gradients and add calibrated noise to ensure privacy guarantees, enabling developers to build machine learning models that protect sensitive training data. It is designed to be scalable and efficient, integrating seamlessly with PyTorch workflows.
Developers should learn Opacus when building machine learning applications that handle sensitive data, such as in healthcare, finance, or social media, where privacy regulations like GDPR or HIPAA apply. It is essential for implementing differential privacy in PyTorch models to prevent data leakage and ensure compliance, making it a key tool for privacy-preserving AI research and deployment.