Regression with Label Differential Privacy

• URL: http://arxiv.org/abs/2212.06074v3
• Date: Wed, 4 Oct 2023 18:45:53 GMT
• Title: Regression with Label Differential Privacy
• Authors: Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V Varadarajan, Chiyuan Zhang
• Abstract summary: 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"
• Score: 64.21020761920322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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