Search Results for author:
Found 3 papers, 1 papers with code
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy
Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern.
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling
A classic mechanism for this purpose is DP-SGD, which is a differentially private version of the stochastic gradient descent (SGD) optimizer commonly used for DNN training.
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
