May 10, 2024, 4:11 a.m. | Haoqi Wu, Wenjing Fang, Yancheng Zheng, Junming Ma, Jin Tan, Yinggui Wang, Lei Wang

cs.CR updates on arXiv.org arxiv.org

arXiv:2405.05525v1 Announce Type: new
Abstract: 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 function approximations. However, the integration of quantization widely used in plaintext inference into the MPC domain remains unclear. To bridge this gap, we propose the framework named Ditto to enable more efficient quantization-aware secure Transformer …

arxiv aware client computation cs.cr cs.lg data ditto enable function integration linear mpc non party privacy privacy concerns rising sensitive techniques transformers

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