NEURAL NETWORKS WITH HOMOMORPHIC ENCRYPTION

VAlue proposition

Deployment of neural inference over encrypted data—using Fully Homomorphic Encryption (FHE) or Secure Multiparty Computation (MPC)—enables privacy-preserving computation crucial for sensitive applications in healthcare, biometrics, finance, and national security. However, encrypted neural inference has significant physical consequences: High power consumption, causing excessive energy use, increased heat, and additional cooling infrastructure. Large hardware footprints requiring increased physical space, server racks, and supporting facilities are needed. There is decreased hardware reliability and lifespan, due to intensive computational loads that accelerate hardware wear. These physical problems exponentially worsen when considering variations across cryptographic schemes, hardware platforms, and neural architectures.

Description of Technology

This technology is an automated optimization system that directly reduces physical resource usage by intelligently generating highly efficient encrypted inference implementations. It specifically achieves: Reduced energy usage by minimizing computational intensity through optimal selection of cryptographic parameters (e.g., bootstrapping frequency, polynomial approximations). Smaller physical footprints through hardware-optimized circuit designs, reducing the number of servers, processors, and related infrastructure needed. Improved hardware lifespan by significantly decreasing computational load and thermal stress on computing components, extending operational reliability.

Benefits

Automated cross-scheme optimization (e.g., CKKS, BGV, TFHE, SPDZ, GMW)
Hardware-aware automated optimization, reducing physical resources (CPUs, GPUs, FPGAs, ASICs)
Adaptive optimization to support emerging cryptographic, hardware, and neural innovations
Physical-resource-centric optimization framework

Applications