EncFormer: Secure and Efficient Transformer Inference over Encrypted Data

EncFormer addresses a critical bottleneck in Privacy-Preserving Machine Learning (PPML): the 'representation switching' cost between FHE (used for linear layers) and MPC (used for non-linear layers like Softmax/GELU). While the project has 0 stars, the 4 forks within 4 days of release indicate immediate academic/peer interest typically seen with new ArXiv publications. Its defensibility is rooted in deep domain expertise (cryptographic protocol design), but it lacks a commercial moat or network effects common in higher-scored projects. It competes with established research frameworks like Cheetah, Iron, and Bolt. The primary risk is that major cloud providers (AWS, Azure) are already integrating confidential computing (TEE) or their own FHE implementations (e.g., Microsoft SEAL), which could render specialized hybrid frameworks like this obsolete if hardware-accelerated FHE makes the MPC conversion unnecessary. However, EncFormer's 'Stage Compatible Patterns' represent a meaningful architectural improvement for current-gen secure inference.