差分私有直方图的工程方法：效率超越效用
Engineering Methods for Differentially Private Histograms: Efficiency Beyond Utility
摘要：
出版直方图与$ε$-差分隐私已被广泛研究的文献。现有的计划的目的是最大限度地提高公布的数据的效用，而以前的实验评估分析的隐私/效用权衡。在本文中，我们提供了第一个实验评估的差异私人的方法，超越实用程序，还强调另一个重要的方面，即效率。为此，我们首先注意到，所有现有的计划都是由一小部分公共块组成的。然后，我们优化并选择每个块的最佳实现，确定捕获整个文献的块的组合，并提出新的块组合。我们根据效率和效用的天际线，即，基于方法是否在两个方面都占主导地位。通过对四个具有不同特征的真实数据集进行详尽的实验，我们得出结论，在效用和效率方面总是存在权衡。我们证明了我们的新块组合的方案，为时间关键的应用程序提供了最佳的权衡。我们的工作可以作为一个指导，以帮助从业者工程师根据他们的应用程序的要求，不同的私人直方图计划。
英文摘要：
Publishing histograms with $ε$-differential privacy has been studied extensively in the literature. Existing schemes aim at maximizing the utility of the published data, while previous experimental evaluations analyze the privacy/utility trade-off. In this paper we provide the first experimental evaluation of differentially private methods that goes beyond utility, emphasizing also on another important aspect, namely efficiency. Towards this end, we first observe that all existing schemes are comprised of a small set of common blocks. We then optimize and choose the best implementation for each block, determine the combinations of blocks that capture the entire literature, and propose novel block combinations. We qualitatively assess the quality of the schemes based on the skyline of efficiency and utility, i.e., based on whether a method is dominated on both aspects or not. Using exhaustive experiments on four real datasets with different characteristics, we conclude that there are always trade-offs in terms of utility and efficiency. We demonstrate that the schemes derived from our novel block combinations provide the best trade-offs for time critical applications. Our work can serve as a guide to help practitioners engineer a differentially private histogram scheme depending on their application requirements.
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