Dec 9, 2023 · We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP). In ...
We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP). In particular, we leverage ...
Feb 16, 2024 · Summary: The paper studies the regression problems under label differential privacy (DP). It proposes a novel randomizer that generates high-quality DP labels ...
May 30, 2024 · We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP).
Optimal Unbiased Randomizers for Regression with Label Differential ...
www.emergentmind.com › papers
Dec 9, 2023 · We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP).
Optimal Unbiased Randomizers for Regression with Label Differential Privacy. Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman ...
This package contains the Matlab code used to reproduce the experimental results of the following paper: Wang, Yu-Xiang. "Revisiting differentially private ...
Optimal Unbiased Randomizers for Regression with Label Differential Privacy. 05:05. Optimal Unbiased Randomizers for Regression with Label Differential Privacy.
Optimal unbiased randomizers for regression with label differential privacy. A Badanidiyuru, B Ghazi, P Kamath, R Kumar, E Leeman, P Manurangsi, ... arXiv ...
User-Level Differential Privacy With Few Examples Per User · Counterfactual Memorization in Neural Language Models · Optimal Unbiased Randomizers for Regression ...