ICLR 2023poster17 citations
Regression with Label Differential Privacy
Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang
Abstract
We study the task of training regression models with the guarantee of _label_ differential privacy (DP). Based on a global prior distribution of label values, which could be obtained privately, we derive a label DP randomization mechanism that is optimal under a given regression loss function. We prove that the optimal mechanism takes the form of a "randomized response on bins", and propose an efficient algorithm for finding the optimal bin values. We carry out a thorough experimental evaluation on several datasets demonstrating the efficacy of our algorithm.
label differential privacyregression
BibTeX
@inproceedings{
ghazi2023regression,
title={Regression with Label Differential Privacy},
author={Badih Ghazi and Pritish Kamath and Ravi Kumar and Ethan Leeman and Pasin Manurangsi and Avinash Varadarajan and Chiyuan Zhang},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=h9O0wsmL-cT}
}