Search Results for author:
Found 6 papers, 1 papers with code
Sharper Utility Bounds for Differentially Private Models
In this paper, by introducing Generalized Bernstein condition, we propose the first $\mathcal{O}\big(\frac{\sqrt{p}}{n\epsilon}\big)$ high probability excess population risk bound for differentially private algorithms under the assumptions $G$-Lipschitz, $L$-smooth, and Polyak-{\L}ojasiewicz condition, based on gradient perturbation method.
Stability and Generalization of Differentially Private Minimax Problems
To the best of our knowledge, this is the first time to analyze the generalization performance of general minimax paradigm, taking differential privacy into account.
Towards Sharper Utility Bounds for Differentially Private Pairwise Learning
Pairwise learning focuses on learning tasks with pairwise loss functions, depends on pairs of training instances, and naturally fits for modeling relationships between pairs of samples.
Data Heterogeneity Differential Privacy: From Theory to Algorithm
Traditionally, the random noise is equally injected when training with different data instances in the field of differential privacy (DP).
Input Perturbation: A New Paradigm between Central and Local Differential Privacy
By adding noise to the original training data and training with the `perturbed data', we achieve ($\epsilon$,$\delta$)-differential privacy on the final model, along with some kind of privacy on the original data.
Weighted Distributed Differential Privacy ERM: Convex and Non-convex
By detailed theoretical analysis, we show that in distributed setting, the noise bound and the excess empirical risk bound can be improved by considering different weights held by multiple parties.
