This research introduces a novel approach to dramatically speed up Number Theoretic Transform (NTT) implementation—a critical bottleneck in privacy-preserving machine learning systems.

• GPU-accelerated algorithms that enhance homomorphic encryption performance
• LLM-powered code generation to automate complex cryptographic implementations
• System-agnostic approach that works across different GPUs and operating systems
• Practical security gains for privacy-preserving machine learning applications

The innovation addresses a critical challenge in security: making homomorphic encryption efficient enough for real-world adoption in privacy-sensitive applications, accelerating the path to truly secure AI systems.

Ramp Up NTT in Record Time using GPU-Accelerated Algorithms and LLM-based Code Generation