Accurate BGV Parameters Selection: Accounting for Secret and Public Key Dependencies in Average-Case Analysis

This project is a specialized academic implementation focused on the mathematical refinement of BGV (Fully Homomorphic Encryption) parameters. Its defensibility is low (3) because, while it involves deep domain expertise in lattice-based cryptography, it is essentially a reference implementation for a specific research paper. With 0 stars and 4 forks, it has zero market adoption outside of potential academic citations. Frontier labs (OpenAI, Anthropic) are unlikely to compete directly as they focus on high-level application layers or hardware-accelerated inference rather than the low-level parameter tuning of specific FHE schemes. However, existing robust FHE libraries like OpenFHE, Microsoft SEAL, or Lattigo represent significant competition; if this research is valid, these established libraries will simply incorporate the improved noise bounds as a library update, effectively neutralizing the project as a standalone tool. The value lies in the 'accuracy' of the noise analysis, which allows for smaller parameters (and thus better performance), but this is a feature, not a product.