no code implementations • 17 Mar 2024 • Xuanqi Liu, Zhuotao Liu, Qi Li, Ke Xu, Mingwei Xu
In this paper, we present Pencil, the first private training framework for collaborative learning that simultaneously offers data privacy, model privacy, and extensibility to multiple data providers, without relying on the non-colluding assumption.
1 code implementation • 28 May 2023 • Xuanqi Liu, Zhuotao Liu
The community explored to build private inference frameworks for transformer-based large language models (LLMs) in a server-client setting, where the server holds the model parameters and the client inputs its private data (or prompt) for inference.
no code implementations • 23 Jan 2019 • Ke-Wei Huang, Mengke Qiao, Xuanqi Liu, Siyuan Liu, Mingxi Dai
This study provides convincing evidence that the proposed method could objectively create a powerful test statistic based on Q-Q plots and this method could be modified to construct many more powerful test statistics for other applications in the future.