Fast Networks for High-Performance Distributed Trust

2025-10-31

AI Summary

Inter-organizational collaborative computing faces a fundamental trade-off between data privacy and computational performance: existing distributed trust encryption techniques fail to meet the high-throughput, low-latency requirements of federated data analytics and AI workloads. To address this, we propose the Distributed-but-Proximate Trust (DBPT) model—a LAN-centric trust architecture designed for datacenter environments. DBPT integrates lightweight cryptographic primitives, cross-domain secure communication protocols, and co-designed computation optimization mechanisms, enabling efficient and secure collaboration while preserving strict physical data isolation among participants. Compared to naive LAN-direct baselines, DBPT achieves a tenfold improvement in computational throughput. This advance significantly enhances the practicality of privacy-preserving collaborative AI and analytics, and further opens a new pathway toward high-performance quantum-safe systems.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Search and Optimization: Distributed Search
• Machine Learning: Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

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 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.

Problem

• Enabling secure computation on combined datasets while preserving data privacy
• Achieving high-performance distributed trust beyond current cryptographic limitations
• Developing LAN-based deployment models for physically/logically separate collaboration

Innovation

• Redesigned distributed trust frameworks for LANs
• Developed deployment models for proximate trust
• Enabled secure collaborative analytics with high performance