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
EVA-S3PC: Efficient, Verifiable, Accurate Secure Matrix Multiplication Protocol Assembly and Its Application in Regression
Shizhao Peng, Tianrui Liu, Tianle Tao +3
Efficient multi-party secure matrix multiplication is crucial for privacy-preserving machine learning, but existing mixed-protocol frameworks often face challenges in balancing sec…
cs.CR2023
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…