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Regression with Label Differential Privacy. (arXiv:2212.06074v3 [cs.LG] UPDATED)
cs.CR updates on arXiv.org arxiv.org
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 …
differential privacy distribution function global guarantee loss mechanism privacy prove randomization study task training under