8 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.CR2024
Nimbus: Secure and Efficient Two-Party Inference for Transformers
Zhengyi Li, Kang Yang, Jin Tan +8
Transformer models have gained significant attention due to their power in machine learning tasks. Their extensive deployment has raised concerns about the potential leakage of sen…
cs.CR2024
Ditto: Quantization-aware Secure Inference of Transformers upon MPC
Haoqi Wu, Wenjing Fang, Yancheng Zheng +4
Due to the rising privacy concerns on sensitive client data and trained models like Transformers, secure multi-party computation (MPC) techniques are employed to enable secure infe…
cs.CR2023★ 8 cited
PUMA: Secure Inference of LLaMA-7B in Five Minutes
Ye Dong, Wen-jie Lu, Yancheng Zheng +7
With ChatGPT as a representative, tons of companies have began to provide services based on large Transformers models. However, using such a service inevitably leak users' prompts…