Regression with Label Differential PrivacyDownload PDF

Published: 01 Feb 2023, Last Modified: 27 Feb 2023ICLR 2023 posterReaders: Everyone
Keywords: label differential privacy, regression
TL;DR: We present a new label differentially private algorithm for training regression models.
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.
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