论文标题
在当地差异隐私下，通过模型选择进行自适应光谱密度估算
Adaptive spectral density estimation by model selection under local differential privacy
论文作者
论文摘要
我们研究局部差异隐私的光谱密度估计。匿名是通过截断，然后进行拉普拉斯扰动来实现的。我们通过惩罚对比标准从一组候选估计器中选择估计器。显示该估计器的收敛速率与候选人集的最佳估计器几乎相同。证明的关键要素是根据Arxiv中获得的次指数随机变量的浓度的最新结果：1903.05964。我们在一项小型仿真研究中说明了我们的发现。
We study spectral density estimation under local differential privacy. Anonymization is achieved through truncation followed by Laplace perturbation. We select our estimator from a set of candidate estimators by a penalized contrast criterion. This estimator is shown to attain nearly the same rate of convergence as the best estimator from the candidate set. A key ingredient of the proof are recent results on concentration of quadratic forms in terms of sub-exponential random variables obtained in arXiv:1903.05964. We illustrate our findings in a small simulation study.
