Scalable Differential Privacy: A Technical Performance Evaluation of Opacus

We examine its core architecture, mathematical
foundations, advanced scalability mechanisms, performance characteristics,
security features, and real-world deployment patterns. Through detailed
analysis of the make_private transformation, Ghost Clipping for LLMs,
and formal privacy accounting, we demonstrate how Opacus establishes the
industrial standard for privacy-preserving deep learning—enabling
organizations to meet rigorous regulatory requirements without sacrificing
model utility.