The Future of Fully Homomorphic Encryption System: from a Storage I/O Perspective

The project addresses a critical but often ignored 'unsexy' problem in the FHE ecosystem: the storage I/O bottleneck. While the industry is obsessed with reducing NTT (Number Theoretic Transform) latency and ciphertext expansion, this project quantifies how data movement from storage to compute units can throttle performance by orders of magnitude (up to 357x for ASICs). With 0 stars but 11 forks, the project shows characteristics of a targeted research artifact being used by other academic or industrial labs rather than a general-purpose tool. Its defensibility is low because it is primarily a diagnostic study; the 'moat' is the insight, not the code itself. Frontier labs (OpenAI/Google) are currently focused on LLM privacy through TEEs or Differential Privacy and are unlikely to build FHE storage benchmarks themselves. However, dedicated FHE hardware players like Zama, Optalysys, or Cornami will likely absorb these insights into their next-generation hardware-software co-design, making this specific benchmarking repo obsolete within 1-2 years as integrated 'FHE-on-storage' solutions emerge. Platform domination risk is medium because cloud providers like AWS/Azure could eventually implement specialized NVMe-over-Fabric protocols optimized for FHE ciphertext chunks, effectively solving the problem at the infrastructure layer.