"Private, Efficient, and Accurate: Protecting Models Trained by Multi-party ..."

Wenqiang Ruan et al. (2022)

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DOI: 10.48550/ARXIV.2208.08662

access: open

type: Informal or Other Publication

metadata version: 2024-08-13

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Wenqiang Ruan, Mingxin Xu, Wenjing Fang, Li Wang, Lei Wang, Weili Han:
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy. CoRR abs/2208.08662 (2022)

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