论文标题
在高维度上设计差异私人估计器
Designing Differentially Private Estimators in High Dimensions
论文作者
论文摘要
我们在高维环境中研究差异私人平均值估计。现有的差异隐私技术适用于大维度，导致计算上棘手的问题或估计器过多的隐私损失。高维鲁棒统计数据的最新工作已经确定了具有渐近维度误差保证的计算含量估计算法。我们将这些结果纳入了严格的结合，以鲁棒平均估计器的全局灵敏度结合。这产生了一种可在高维度中具有差异私人均值估计的计算算法，并具有无关维度的隐私损失。最后，我们显示合成数据表明，我们的算法显着胜过经典的差异隐私方法，克服了高维差异隐私的障碍。
We study differentially private mean estimation in a high-dimensional setting. Existing differential privacy techniques applied to large dimensions lead to computationally intractable problems or estimators with excessive privacy loss. Recent work in high-dimensional robust statistics has identified computationally tractable mean estimation algorithms with asymptotic dimension-independent error guarantees. We incorporate these results to develop a strict bound on the global sensitivity of the robust mean estimator. This yields a computationally tractable algorithm for differentially private mean estimation in high dimensions with dimension-independent privacy loss. Finally, we show on synthetic data that our algorithm significantly outperforms classic differential privacy methods, overcoming barriers to high-dimensional differential privacy.
