Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy

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.

Federated Learning Math

Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy

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.

Federated Learning Math +1

Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs

no code implementations • 2 Nov 2018 • Ahmad Al Badawi, Jin Chao, Jie Lin, Chan Fook Mun, Jun Jie Sim, Benjamin Hong Meng Tan, Xiao Nan, Khin Mi Mi Aung, Vijay Ramaseshan Chandrasekhar

In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs.

Privacy Preserving