Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Sadia, Mushtari, Chowdhury, Amrita Roy
Institution: University of Michigan
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7213319586
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