论文标题
带有标签差异隐私的回归
Regression with Label Differential Privacy
论文作者
论文摘要
我们通过保证标签差异隐私（DP）来研究培训回归模型的任务。基于可以私下获得的标签值的全局先验分布，我们得出了在给定的回归损失函数下最佳的标签DP随机机制。我们证明，最佳机制采用“箱上的随机响应”的形式，并提出了一种有效的算法来查找最佳垃圾箱值。我们对几个数据集进行了彻底的实验评估，以证明我们的算法的功效。
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.
