EncFormer: Secure and Efficient Transformer Inference over Encrypted Data

EncFormer addresses a critical bottleneck in Private AI: the massive overhead introduced when switching between FHE (efficient for linear layers) and MPC (efficient for non-linear layers like Softmax/GELU). By introducing 'Stage Compatible Patterns,' it seeks to minimize the costly conversion steps that plague previous state-of-the-art frameworks like Cheetah or Iron. Despite having 0 stars, the 4 forks within 6 days of an arXiv-linked release suggest immediate peer review/research interest. The defensibility is currently low (4) because it is a research-centric reference implementation; its value lies in the mathematical optimizations which can be absorbed by larger libraries like Meta's CrypTen or Microsoft's SEAL. Frontier labs are unlikely to adopt FHE for consumer-scale inference today due to the latency penalty (often 100x-1000x), but as hardware acceleration (ASICs for FHE) matures, the algorithms introduced here will become part of the standard stack for 'Confidential MLaaS.' The primary threat is from academic competitors (e.g., the Berkeley/Microsoft/Alibaba research groups) who frequently iterate on these hybrid protocols, making any specific optimization obsolete within 18-24 months.