Regression with Label Differential Privacy

2022/12/12 by Ghazi, Badih, Kamath, Pritish, Kumar, Ravi +4 · 1 citation
#Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2212.06074

Abstract

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.

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• GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy

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