Designing polynomial activation functions (GELU/ReLU approximations) for homomorphic evaluation.
The Encrypted ML Inferences architecture is engineered to provide cryptographically provable security based on worst-case lattice hardness. By replacing vulnerable discrete logarithm and factorization primitives with high-dimensional Euclidean lattice vectors, the system guarantees long-term confidentiality against cryptanalytically relevant quantum computers.
Deploying Encrypted ML Inferences requires careful balancing of public key size overhead, network MTU fragmentation limits, and memory cache footprint. Through hardware coprocessor offload and vectorized assembly pipelines, production systems achieve near-zero performance degradation compared to legacy classical ciphers.