Organizations increasingly need to collaborate by performing a computation on their combined dataset, while keeping their data hidden from each other. Certain kinds of collaboration, such as collaborative data analytics and AI, require a level of performance beyond what current cryptographic techniques for distributed trust can provide. This is because the organizations run software in different trust domains, which can require them to communicate over WANs or the public Internet. In this paper, we explore how to instead run such applications using fast datacenter-type LANs. We show that, by carefully redesigning distributed trust frameworks for LANs, we can achieve up to order-of-magnitude better performance than naïvely using a LAN. Then, we develop deployment models for Distributed But Proximate Trust (DBPT) that allow parties to use a LAN while remaining physically and logically distinct. These developments make secure collaborative data analytics and AI significantly more practical and set new research directions for developing systems and cryptographic theory for high-performance distributed trust.

翻译：组织间日益需要通过对其联合数据集执行计算来实现协作，同时保持各自数据的相互隐蔽性。某些类型的协作，例如协同数据分析与人工智能，对性能的要求已超出当前分布式信任密码学技术所能提供的水平。这是因为各组织在不同信任域中运行软件，可能需要通过广域网或公共互联网进行通信。本文探讨了如何转而利用快速数据中心型局域网运行此类应用。我们证明，通过针对局域网精心重新设计分布式信任框架，可获得比简单使用局域网高出一个数量级的性能提升。随后，我们构建了分布式邻近信任的部署模型，使参与方能够在保持物理与逻辑独立性的前提下利用局域网。这些进展显著提升了安全协同数据分析与人工智能的实用性，并为开发高性能分布式信任的系统与密码学理论确立了新的研究方向。