HELiKs: HE Linear Algebra Kernels for Secure Inference
Shashank Balla, Farinaz Koushanfar
Abstract
We introduce HELiKs, a groundbreaking framework for fast and secure matrix multiplication and 3D convolutions, tailored for privacy-preserving machine learning. Leveraging Homomorphic Encryption (HE) and Additive Secret Sharing, HELiKs enables secure matrix and vector computations while ensuring end-to-end data privacy for all parties. Key innovations of the proposed framework include an efficient multiply-accumulate (MAC) design that significantly reduces HE error growth, a partial sum accumulation strategy that cuts the number of HE rotations by a logarithmic factor, and a novel matrix encoding that facilitates faster online HE multiplications with one-time pre-computation. Furthermore, HELiKs substantially reduces the number of keys used for HE computation, leading to lower bandwidth usage during the setup phase. In our evaluation, HELiKs shows considerable performance improvements in terms of runtime and communication overheads when compared to existing secure computation methods. With our proof-of-work implementation (available on GitHub: https://github.com/shashankballa/HELiKs), we demonstrate state-of-the-art performance with up to 32x speedup for matrix multiplication and 27x speedup for 3D convolution when compared to prior art. HELiKs also reduces communication overheads by 1.5x for matrix multiplication and 29x for 3D convolution over prior works, thereby improving the efficiency of data transfer.