论文标题
羽流：大规模差异隐私
Plume: Differential Privacy at Scale
论文作者
论文摘要
差异隐私已成为私人数据分析的标准，并且广泛的文献现在为各种问题提供了不同的私人解决方案。但是，将这些解决方案转换为实用系统通常需要面对文献忽略或摘要的细节：用户可以贡献多个记录，可能的记录域可能未知，并且最终的系统必须扩展到大量数据。未能仔细考虑这三个问题会严重损害系统的质量和可用性。 我们提出羽流，这是一种旨在解决这些问题的系统。我们描述了许多有时细微的实施问题，并提供了实用的解决方案，这些解决方案共同使工业规模的系统成为可能的私人数据分析。 Plume目前已在Google部署，通常用于处理具有数万亿个记录的数据集。
Differential privacy has become the standard for private data analysis, and an extensive literature now offers differentially private solutions to a wide variety of problems. However, translating these solutions into practical systems often requires confronting details that the literature ignores or abstracts away: users may contribute multiple records, the domain of possible records may be unknown, and the eventual system must scale to large volumes of data. Failure to carefully account for all three issues can severely impair a system's quality and usability. We present Plume, a system built to address these problems. We describe a number of sometimes subtle implementation issues and offer practical solutions that, together, make an industrial-scale system for differentially private data analysis possible. Plume is currently deployed at Google and is routinely used to process datasets with trillions of records.
