论文标题
在当地差异隐私下的最佳私人回归率
On rate optimal private regression under local differential privacy
论文作者
论文摘要
我们考虑在局部微分隐私框架中，从匿名数据估算回归函数的问题。我们提出了回归函数的新颖分区估计值，从Hölder类别中得出过多预测风险的收敛速率，并证明了匹配的下限。与有关该问题的现有文献相反，所谓的强度密度假设对设计分布进行了过时。
We consider the problem of estimating a regression function from anonymized data in the framework of local differential privacy. We propose a novel partitioning estimate of the regression function, derive a rate of convergence for the excess prediction risk over Hölder classes, and prove a matching lower bound. In contrast to the existing literature on the problem the so-called strong density assumption on the design distribution is obsolete.
