Secure Transformer Inference Made Non-interactive
Jiawen Zhang, Xinpeng Yang, Lipeng He, Kejia Chen, Wen-jie Lu, Yinghao Wang, Xiaoyang Hou, Jian Liu, Kui Ren, Xiaohu Yang
Abstract
Secure transformer inference has emerged as a prominent research topic following the proliferation of ChatGPT. Existing solutions are typically interactive, involving substantial communication load and numerous interaction rounds between the client and the server. In this paper, we propose NEXUS , the first non-interactive protocol for secure transformer inference, where the client is only required to submit an encrypted input and await the encrypted result from the server. Central to NEXUS are two innovative techniques: SIMD ciphertext compression/decompression, and SIMD slots folding. Consequently, our approach achieves a speedup of 2.8 × and a remarkable bandwidth reduction of 368.6 × , compared to the state-of-the-art solution presented in S&P ’24.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 51823e82-317b-46a1-bba2-0a2a8ecaa5fdCited by top-tier papers22
- MOAI: Module-Optimizing Architecture for Non-Interactive Secure Transformer InferenceLinru Zhang, Xiangning Wang, Sim Jun Jie, Zhicong Huang et al.ICLR 2026 · 24 citations
- Hydra: Scale-out FHE Accelerator Architecture for Secure Deep Learning on FPGAYinghao Yang, Xicheng Xu, Haibin Zhang, Jie Song et al.HPCA 2025 · 7 citations
- Bridging Usability and Performance: A Tensor Compiler for Autovectorizing Homomorphic EncryptionEdward Chen, Fraser Brown, Wenting ZhengUSENIX Security 2026 · 3 citations
- STIP: Three-Party Privacy-Preserving and Lossless Inference for Large Transformers in ProductionMu Yuan, Lan Zhang, Yihang Cheng, Miao-Hui Song et al.NDSS 2026 · 2 citations
- Fenc2: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment EncodingRan Ran, Zhaoting Gong, Nuo Xu, Yuanchao Xu et al.ISCA 2026
Builds on26
- Membership Inference Attacks Against Machine Learning ModelsReza Shokri, Marco Stronati, Congzheng Song, Vitaly ShmatikovS&P 2017 · 5,137 citations
- GAZELLE: A Low Latency Framework for Secure Neural Network InferenceChiraag Juvekar, Vinod Vaikuntanathan, Anantha P. ChandrakasanUSENIX Security 2018 · 1,075 citations
- Oblivious Neural Network Predictions via MiniONN TransformationsJian Liu, Mika Juuti, Yao Lu, N. AsokanCCS 2017 · 800 citations
- PIR with Compressed Queries and Amortized Query ProcessingSebastian Angel, Hao Chen, Kim Laine, Srinath T. V. SettyS&P 2018 · 353 citations
- F1: A Fast and Programmable Accelerator for Fully Homomorphic EncryptionNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Srinivas Devadas et al.MICRO 2021 · 294 citations
Related papers
- Euston: Efficient and User-Friendly Secure Transformer Inference with Non-InteractivityXinwen Gao, Shaojing Fu, Lin Liu, Zhuotao Liu et al.S&P 2026 · 7 citations
- SHAFT: Secure, Handy, Accurate and Fast Transformer InferenceAndes Y. L. Kei, Sherman S. M. ChowNDSS 2025
- Iron: Private Inference on TransformersMeng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing et al.NeurIPS 2022 · 209 citations
- Primer: Fast Private Transformer Inference on Encrypted DataMengxin Zheng, Qian Lou, Lei JiangDAC 2023 · 26 citations
- An Efficient Private GPT Never Autoregressively DecodesZhengyi Li, Yue Guan, Kang Yang, Yu Feng et al.ICML 2025
