Trinqet: Private Triangle and Quadrangle Counting over Distributed Graphs

2026-09-13

AI Summary

本文提出Trinqet系统，通过新的MPC友好算法解决分布式图中隐私保护下的三角形和四边形计数问题，有效利用图的稀疏性提高效率。

Abstract

Triangle and quadrangle counts are core statistics in graph analysis. Yet many real-world graphs are distributed across multiple parties and encode highly sensitive relationships, precluding direct data sharing. Secure multi-party computation (MPC) provides a principled alternative, but introduces a fundamental tension: the protocol must hide the graph's topology, forcing fully data-oblivious operations over all potential edges--while real-world graphs are extremely sparse, making most of this work wasted. We propose Trinqet, a system that resolves this tension through new MPC-friendly algorithms for private triangle and quadrangle detection. Trinqet safely exploits sparsity using a suite of novel techniques that eliminate vast numbers of unnecessary operations while preserving strong security in the malicious threat model. Trinqet supports both counting and enumeration and, in our evaluation, outperforms all five baselines by up to 10^5 in runtime.

Problem

• Triangle Counting
• Quadrangle Counting
• Distributed Graphs
• Privacy
• Sparsity

Innovation

• Private Triangle and Quadrangle Counting
• Distributed Graphs
• Secure Multi-Party Computation (MPC)
• Exploiting Sparsity
• Data-Oblivious Operations