论文标题
多服务器差异隐私的必要条件
Necessary Conditions in Multi-Server Differential Privacy
论文作者
论文摘要
我们考虑用户与多个服务器通信以对用户数据进行计算的协议。对手对许多各方都具有半honest的控制权，但相对于诚实用户，其观点是私人的。先前的工作描述了需要进行多个互动或针对计算对手提供的隐私的协议。我们的工作提出了非互动协议的局限性，这些方案为无限的对手提供了隐私。我们显示这些协议要求与集中私人对应物相比，对某些学习和估计任务的指数要求更多的样本。这意味着执行以及中央模型需要交互性或计算差异隐私，或两者兼而有之。
We consider protocols where users communicate with multiple servers to perform a computation on the users' data. An adversary exerts semi-honest control over many of the parties but its view is differentially private with respect to honest users. Prior work described protocols that required multiple rounds of interaction or offered privacy against a computationally bounded adversary. Our work presents limitations of non-interactive protocols that offer privacy against unbounded adversaries. We show these protocols demand exponentially more samples for some learning and estimation tasks than centrally private counterparts. This means performing as well as the central model requires interactivity or computational differential privacy, or both.
