Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix

TL;DR

This paper introduces the Noisy Adjacency Matrix (NAM) framework for efficient, accurate subgraph counting under edge local differential privacy, addressing high complexity and low accuracy issues of existing methods.

Contribution

It proposes NAM and five algorithms for counting triangles, quadrangles, and 2-stars with improved accuracy, scalability, and privacy guarantees under edge-LDP.

Findings

TriOR reduces time complexity for triangle counting.

TriTR achieves optimal accuracy in triangle counting.

QuaTR is the first quadrangle counting algorithm under pure edge-LDP.

Abstract

When analyzing connection patterns within graphs, subgraph counting serves as an effective and fundamental approach. Edge-local differential privacy (edge-LDP) and shuffle model have been employed to achieve subgraph counting under a privacy-preserving situation. Existing algorithms are plagued by high time complexity, excessive download costs, low accuracy, or dependence on trusted third parties. To address the aforementioned challenges, we propose the Noisy Adjacency Matrix (NAM), which combines differential privacy with the adjacency matrix of the graph. NAM offers strong versatility and scalability, making it applicable to a wider range of DP variants, DP mechanisms, and graph types. Based on NAM, we designed five algorithms (TriOR, TriTR, TriMTR, QuaTR, and 2STAR) to count three types of subgraphs: triangles, quadrangles, and 2-stars. Theoretical and experimental results demonstrate that in triangle counting, TriOR maximizes accuracy with reduced time complexity among one-round algorithms, TriTR achieves optimal accuracy, TriMTR achieves the highest accuracy under low download costs, and QuaTR stands as the first quadrangle counting algorithm under pure edge-LDP. We implement edge-LDP for noisy data via a confidence interval-inspired method, providing DP guarantees on randomized data. Our 2STAR algorithm achieves the highest accuracy in 2-star counting and can be derived as a byproduct of two-round triangle or quadrangle counting algorithms, enabling efficient joint estimation of triangle, quadrangle, and 2-star counts within two query rounds.