提出高效私有估计方法，适用于任意黑箱函数。
Privately Estimating Black-Box Statistics
- 设计可平衡数据效率与查询次数的私有估计方案。
- 在保证差分隐私前提下，显著降低对函数的评估次数。
- 理论证明接近最优，适合高敏感度或未知函数场景。
标准的差分隐私估计方法（如拉普拉斯或高斯噪声添加）需要已知估计器的敏感度上界，但此类边界常过大或不可知。因此，我们寻求适用于任意黑箱函数的差分隐私方法。现有技术要么数据利用效率低，要么需对指数级数量的输入进行函数评估。本文提出一种可在统计效率（所需数据量）与查询效率（函数评估次数）间权衡的方案，并给出下界证明其近似最优性。
原文摘要 · Abstract (English)
Standard techniques for differentially private estimation, such as Laplace or Gaussian noise addition, require guaranteed bounds on the sensitivity of the estimator in question. But such sensitivity bounds are often large or simply unknown. Thus we seek differentially private methods that can be applied to arbitrary black-box functions. A handful of such techniques exist, but all are either inefficient in their use of data or require evaluating the function on exponentially many inputs. In this work we present a scheme that trades off between statistical efficiency (i.e., how much data is needed) and oracle efficiency (i.e., the number of evaluations). We also present lower bounds showing the near-optimality of our scheme.
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