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Regression with Label Differential Privacy
Badih Ghazi · Pritish Kamath · Ravi Kumar · Ethan Leeman · Pasin Manurangsi · Avinash Varadarajan · Chiyuan Zhang
Keywords: [ Social Aspects of Machine Learning ] [ regression ] [ label differential privacy ]
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.