Federated analytics relies on the collection of accurate statistics about distributed users with a suitable guarantee. In this paper, we show how a strong $(\epsilon, \delta)$-privacy guarantee can be achieved for the fundamental problem of histogram generation in a federated setting, via a highly practical sampling-based procedure. Given such histograms, related problems such as heavy hitters and quantiles can be answered with provable error and privacy guarantees. Our experimental results demonstrate that this sample-and-threshold approach is both accurate and scalable.
翻译:联邦分析学依靠收集有适当保证的分布用户的准确统计数据。 在本文中,我们展示了如何通过一种非常实用的取样程序,在联盟环境中,为直方图生成的根本问题,通过一种非常实用的取样程序,为直方图生成提供强大的美元( epsilon,\delta) 美元( privacy) 保证。鉴于这些直方图,重击手和四分位数等相关问题可以用可辨的错误和隐私保障来解答。我们的实验结果显示,这种抽样和临界值方法既准确又可伸缩。