提出可计算的鲁棒差分隐私均值估计方法，抗异常值且性能更优。
Computationally tractable robust differentially private mean estimation
- 通过扩张马氏距离球迭代裁剪，实现可计算的差分隐私
- 在重尾和污染椭球模型下保持良好统计性能，优于现有方法
- 参数少且可解释，适合高维数据中含异常值的隐私保护场景
我们提出一种新的差分隐私均值估计器——气球均值（balloon mean）。其核心特点是计算可处理且对异常观测具有鲁棒性。该方法基于在不断扩张的马氏距离球（即“气球”）上进行迭代裁剪。该方法满足零集中差分隐私，仅依赖少数可解释的调参。我们在重尾和污染椭球模型下提供了理论保证，刻画了其统计性能与抗异常值能力。大量模拟实验表明，气球均值对重尾和污染数据具有鲁棒性，在污染场景下显著优于现有差分隐私均值估计器。
原文摘要 · Abstract (English)
We develop a new, differentially private mean estimator called the balloon mean. The main features of the balloon mean are that it is computationally tractable and enjoys robustness to outlying observations. It is based on an iterative clipping procedure over expanding Mahalanobis balls, or ``balloons.'' The method satisfies zero-concentrated differential privacy and depends on a small number of interpretable tuning parameters. We provide theoretical guarantees under heavy-tailed and contaminated elliptical models, characterizing its statistical performance and robustness to outliers. Extensive simulations demonstrate that the balloon mean is robust to heavy-tailed and contaminated data, and outperforms existing differentially private mean estimators in contaminated settings.
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