We study the task of training regression models with the guarantee of label differential privacy (DP). Based on a global prior distribution on 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.
@article{arxiv.2212.06074,
title = {Regression with Label Differential Privacy},
author = {Badih Ghazi and Pritish Kamath and Ravi Kumar and Ethan Leeman and Pasin Manurangsi and Avinash V Varadarajan and Chiyuan Zhang},
journal= {arXiv preprint arXiv:2212.06074},
year = {2023}
}