论文标题
Duff：基于数据集距离的实用程序功能家族，用于指数机制
Duff: A Dataset-Distance-Based Utility Function Family for the Exponential Mechanism
论文作者
论文摘要
我们建议和分析一个基于通用数据集距离的公用事业功能家族Duff，以差异性隐私的指数机制。鉴于特定的数据集和统计数据（例如中位数，模式），此功能系列根据必须将数据添加到数据集或从数据集中删除的个体数量为可能的输出O分配给可能的输出o，以便统计量以占据值o。我们表明，与基于平稳敏感性的现有差异隐私机制相比，基于DUFF的指数机制通常可以为统计数据的真实价值提供更高的保真度。尤其是，达夫是对是否有可能具有噪声分布的肯定答案，其差异与平滑灵敏度成正比，其尾巴以比多项式速度快的速度衰减。我们以对计算中值任务的DUFF的实际优势进行经验评估来结束论文。
We propose and analyze a general-purpose dataset-distance-based utility function family, Duff, for differential privacy's exponential mechanism. Given a particular dataset and a statistic (e.g., median, mode), this function family assigns utility to a possible output o based on the number of individuals whose data would have to be added to or removed from the dataset in order for the statistic to take on value o. We show that the exponential mechanism based on Duff often offers provably higher fidelity to the statistic's true value compared to existing differential privacy mechanisms based on smooth sensitivity. In particular, Duff is an affirmative answer to the open question of whether it is possible to have a noise distribution whose variance is proportional to smooth sensitivity and whose tails decay at a faster-than-polynomial rate. We conclude our paper with an empirical evaluation of the practical advantages of Duff for the task of computing medians.
