Triple-Hoisted Baby-Step Giant-Step Linear Transformation over CKKS Homomorphic Encryption and Hardware Accelerator
Researchers have introduced a triple-hoisted baby-step giant-step algorithm that further decomposes the baby step to significantly reduce the number of ciphertext rotations required for linear transformations in CKKS homomorphic encryption. They also propose a memory-optimized data path and an FPGA-based hardware accelerator, which together reduce off-chip memory access by 2.9x and computational latency by 5.8x compared to previous designs.
Why it matters: This work represents a notable advance in making privacy-preserving neural network inference more efficient by addressing key computational and memory bottlenecks in homomorphic encryption.
Full story at: arXiv Cryptography and Security ↗