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Yancheng Zheng

2 accepted papers

2024

Ditto: Quantization-aware Secure Inference of Transformers upon MPC

ICML 2024poster

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 inference despite attendant overhead. Existing works attempt to reduce the overhead using more MPC-friendly non-linear funct…

2024

Nimbus: Secure and Efficient Two-Party Inference for Transformers

NeurIPS 2024poster

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 sensitive information during inference. However, when being applied to Transformers, existing approaches based on secure tw…