Paper 2026/046

Euston: Efficient and User-Friendly Secure Transformer Inference with Non-Interactivity

Abstract

Secure TransFormer Inference (STFI) frameworks have been proposed to address privacy concerns over user inputs and model parameters in Transformer-based LLMs. While most existing solutions rely on interactive protocols that incur substantial user-server communication overhead, non-interactive STFI variants have recently emerged to eliminate such dependencies. Nevertheless, state-of-the-art non-interactive STFI frameworks still suffer from critical limitations. (i) Large ciphertext sizes and multiple rotations alongside heavy user-side overhead in Homomorphic Matrix Multiplication (HMM). (ii) High approximation costs and depth consumptions in Homomorphic Nonlinear Evaluations (HNE). To address these limitations, we present Euston, an efficient and user-friendly STFI with non-interactivity. By combining RNS-CKKS fully homomorphic encryption with optimized methods, Euston achieves unprecedented efficiency in offline online inference paradigm. The key innovations are twofold. (i) For linear operations, we adopt Singular Value Decomposition (SVD) with our novel batched HMMs to minimize ciphertext size and reduce rotation counts, simultaneously lowering user-side computational, communication and storage overhead. (ii) For nonlinear operations, we employ column(diagonal)-packed ciphertext matrix formats to eliminate costly rotations and depth regulation strategies to reduce depth consumption in non-interactive HNEs, which not only avoids user-server communications but also accelerates inference performance. In comparision with the state-of-the-art approach (NEXUS, NDSS 2025), Euston achieves up to 3100× lower preprocessing costs for the user and 8.8× higher system-wide inference performance, specifically delivering a 90× speedup for HMM and a 165.7× speedup for HNE. Our results demonstrate that Euston establishes new efficiency frontiers for user-friendly STFI deployment across cloud and edge environments.

Note: To appear in the 47th IEEE Symposium on Security and Privacy (S&P 2026).