发表机构
University of Michigan, Ann Arbor（密歇根大学安娜堡分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Trinqet提出MPC友好的稀疏感知算法，在恶意威胁模型下实现分布式图的私有三角形和四边形计数与枚举，性能优于基线达10^5倍。
AI 中文摘要
三角形和四边形计数是图分析中的核心统计量。然而，许多现实世界的图分布在多个参与方之间，并编码了高度敏感的关系，从而排除了直接数据共享的可能性。安全多方计算（MPC）提供了一种原则性的替代方案，但引入了根本性的张力：协议必须隐藏图的拓扑结构，迫使对所有潜在边进行完全数据无关的操作——而现实世界的图极其稀疏，使得大部分工作被浪费。我们提出了Trinqet，一个通过新的MPC友好算法来解决这一张力的系统，用于私有三角形和四边形检测。Trinqet利用一系列新技术安全地利用稀疏性，消除了大量不必要的操作，同时在恶意威胁模型下保持强安全性。Trinqet支持计数和枚举，在我们的评估中，其运行时间优于所有五个基线，最高达10^5倍。
英文摘要
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.
