Fast Networks for High-Performance Distributed Trust

TL;DR

This paper demonstrates how redesigning distributed trust frameworks for LANs can significantly improve performance for secure collaborative data analytics and AI, enabling practical high-speed distributed trust applications.

Contribution

It introduces deployment models for Distributed But Proximate Trust (DBPT) that enable LAN-based secure collaboration while maintaining physical and logical separation.

Findings

Achieved up to tenfold performance improvements over naive LAN implementations.

Developed deployment models for secure LAN-based distributed trust.

Set new directions for high-performance cryptographic systems.

Abstract

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 na\"ively 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.