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
摘要
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.
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引用它的顶会 Paper22
- MOAI: Module-Optimizing Architecture for Non-Interactive Secure Transformer InferenceLinru Zhang, Xiangning Wang, Sim Jun Jie, Zhicong Huang 等ICLR 2026 · 被引用 24 次
- Hydra: Scale-out FHE Accelerator Architecture for Secure Deep Learning on FPGAYinghao Yang, Xicheng Xu, Haibin Zhang, Jie Song 等HPCA 2025 · 被引用 7 次
- Bridging Usability and Performance: A Tensor Compiler for Autovectorizing Homomorphic EncryptionEdward Chen, Fraser Brown, Wenting ZhengUSENIX Security 2026 · 被引用 3 次
- STIP: Three-Party Privacy-Preserving and Lossless Inference for Large Transformers in ProductionMu Yuan, Lan Zhang, Yihang Cheng, Miao-Hui Song 等NDSS 2026 · 被引用 2 次
- Fenc2: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment EncodingRan Ran, Zhaoting Gong, Nuo Xu, Yuanchao Xu 等ISCA 2026
它引用的顶会 Paper26
- Membership Inference Attacks Against Machine Learning ModelsReza Shokri, Marco Stronati, Congzheng Song, Vitaly ShmatikovS&P 2017 · 被引用 5,137 次
- GAZELLE: A Low Latency Framework for Secure Neural Network InferenceChiraag Juvekar, Vinod Vaikuntanathan, Anantha P. ChandrakasanUSENIX Security 2018 · 被引用 1,075 次
- Oblivious Neural Network Predictions via MiniONN TransformationsJian Liu, Mika Juuti, Yao Lu, N. AsokanCCS 2017 · 被引用 800 次
- PIR with Compressed Queries and Amortized Query ProcessingSebastian Angel, Hao Chen, Kim Laine, Srinath T. V. SettyS&P 2018 · 被引用 353 次
- F1: A Fast and Programmable Accelerator for Fully Homomorphic EncryptionNikola Samardzic, Axel Feldmann, Aleksandar Krastev, Srinivas Devadas 等MICRO 2021 · 被引用 294 次
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