no code implementations • 26 Nov 2023 • Yizheng Zhu, Yuncheng Wu, Zhaojing Luo, Beng Chin Ooi, Xiaokui Xiao
In this paper, we propose a novel and highly efficient solution RiseFL for secure and verifiable data collaboration, ensuring input privacy and integrity simultaneously. Firstly, we devise a probabilistic integrity check method that significantly reduces the cost of ZKP generation and verification.
no code implementations • 8 Dec 2022 • Ergute Bao, Yizheng Zhu, Xiaokui Xiao, Yin Yang, Beng Chin Ooi, Benjamin Hong Meng Tan, Khin Mi Mi Aung
Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern.
no code implementations • 29 Sep 2021 • Ergute Bao, Yizheng Zhu, Xiaokui Xiao, Yin Yang, Beng Chin Ooi, Benjamin Hong Meng Tan, Khin Mi Mi Aung
We point out a major challenge in this problem setting: that common mechanisms for enforcing DP in deep learning, which require injecting \textit{real-valued noise}, are fundamentally incompatible with MPC, which exchanges \textit{finite-field integers} among the participants.
no code implementations • 21 May 2020 • Jacob Black, Shichao Chen, Joseph G. Thomas, Yizheng Zhu
Analytical phase demodulation algorithms in optical interferometry typically fail to reach the theoretical sensitivity limit set by the Cram\'er-Rao bound (CRB).