📄️ Automatic Tuning
Achieving optimal performance in traditional FHE programs requires carefully balancing multiple factors, including the choice of FHE scheme (e.g., CKKS, BFV, BGV, etc.), FHE optimization passes, compiler optimization parameters (e.g., O3, O2, Os, etc.), and the FHE implementation library. Since different applications perform differently with specific FHE schemes, no universal algorithm can deliver optimal results across all scenarios. Additionally, optimization priorities vary
📄️ Efficient Cryptographic Library
Aegis currently supports leading FHE libraries, such as OpenFHE and SEAL, offering users reliable and mature privacy-preserving computing capabilities. In parallel, Aegis is developing its own high-performance FHE library. The goal is to surpass the performance of current mainstream FHE libraries by leveraging innovative algorithmic optimizations and deep hardware acceleration, significantly enhancing the efficiency of fully homomorphic encryption and enabling more efficient privacy computing.