Orbit: Optimizing Rescale and Bootstrap Placement with Integer Linear Programming Techniques for Secure Inference

본문 미리보기

Fully Homomorphic Encryption (FHE) allows computation on encrypted data without decrypting it. In theory, FHE makes privacy-preserving machine learning possible. In practice, however, it remains impractically slow for real workloads. A major source of slowdown is bootstrap operations; in CKKS, a popular FHE scheme for tensor workloads, the slowdown is compounded by scale management and rescale operations. FHE compilers for machine learning inference aim to make bootstrap placement and scale m