论文标题
以设备为中心的联邦分析
Device-centric Federated Analytics At Ease
论文作者
论文摘要
如今，移动设备可以生成大量和隐私敏感的数据，最好保存在设备上并按需查询。但是，数据分析师仍然缺乏利用此类分布式在设备数据的统一方法。在本文中，我们提出了一个数据查询系统甲板，该系统可以灵活地以设备为中心的联合分析。甲板的关键思想是绕过应用程序开发人员，但允许数据分析师通过集中的查询协调器服务直接提交其分析代码以在设备上运行。 Deck为数据分析师提供了标准API列表，并处理下面的大多数设备特定任务。甲板进一步结合了两种关键技术：（i）混合许可检查机制和强制性跨设备聚合以确保数据隐私； （ii）一个零知识统计模型，明智地交易了设备上的查询延迟和查询资源支出。我们完全实现甲板并将其插入20个受欢迎的Android应用程序中。 1,642名志愿者的野外部署显示，与基线相比，甲板将查询延迟显着减少了30倍。我们的微型基准还表明，甲板的独立开销可以忽略不计。
Nowadays, high-volume and privacy-sensitive data are generated by mobile devices, which are better to be preserved on devices and queried on demand. However, data analysts still lack a uniform way to harness such distributed on-device data. In this paper, we propose a data querying system, Deck, that enables flexible device-centric federated analytics. The key idea of Deck is to bypass the app developers but allow the data analysts to directly submit their analytics code to run on devices, through a centralized query coordinator service. Deck provides a list of standard APIs to data analysts and handles most of the device-specific tasks underneath. Deck further incorporates two key techniques: (i) a hybrid permission checking mechanism and mandatory cross-device aggregation to ensure data privacy; (ii) a zero-knowledge statistical model that judiciously trades off query delay and query resource expenditure on devices. We fully implement Deck and plug it into 20 popular Android apps. An in-the-wild deployment on 1,642 volunteers shows that Deck significantly reduces the query delay by up to 30x compared to baselines. Our microbenchmarks also demonstrate that the standalone overhead of Deck is negligible.
