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This study explores the robustness of learning by symmetric loss on privatedata. Specifically, we leverage exponential mechanism (EM) on private labels.First, we theoretically re-discussed properties of EM when it is used forprivate learning with symmetric loss. Then, we propose numerical guidance ofprivacy budgets corresponding to different data scales and utility guarantees.Further, we conducted experiments on the CIFAR-10 dataset to present the traitsof symmetric loss. Since EM is a more generic differential privacy (DP)technique, it being robust has the potential for it to be generalized, and tomake other DP techniques more robust.

Performances of Symmetric Loss for Private Data from Exponential Mechanism