General Techniques for Reducing Key-Switching Overhead in Privacy-Preserving Two-Party Transformer Inference

Abstract

In secure two-party Transformer inference, linear layers are typically evaluated using Fully Homomorphic Encryption (FHE) through plaintext-ciphertext or ciphertext-ciphertext matrix multiplications, where key switching primarily occurs and dominates computational overhead in both FHE-based and hybrid FHE-MPC systems. Existing optimizations rely heavily on packing-specific algorithms, limiting their general applicability. Targeting this overhead from a packing-independent perspective, we propose a preprocessing-assisted method for secure attention computation. By decomposing attention into precomputable operations and online interactions, this method reduces online inference-phase key switching without modifying existing packing strategies. However, the first method shifting key switching offline introduces additional storage requirements. To address this, we propose storage-communication trade-off techniques that replace large precomputed ciphertexts with modest online communication, enabling flexible deployment under varying resource constraints. While ciphertext-ciphertext matrix multiplication is offloaded to the preprocessing phase in hybrid schemes and the first layer of FHE-based schemes, these operations still persist in the offline stage and subsequent FHE layers. To further optimize it, we propose a fused key-switch technique targeting the multiplication-followed-by-rotation pattern, which frequently arises in existing RNS-CKKS matrix multiplication schemes. By combining relinearization and rotation into a single procedure, this technique reduces the associated computation costs. Analytical evaluations demonstrate that our proposed techniques significantly reduce online key-switch overhead and provide flexible trade-offs between storage and communication without requiring modifications to existing packing strategies.