Detecting Communities under Differential Privacy

TL;DR

This paper introduces novel differentially private algorithms for community detection in complex networks, addressing privacy concerns while maintaining high accuracy, and demonstrates their effectiveness on real-world datasets.

Contribution

The paper proposes two new differentially private community detection methods, LouvainDP and ModDivisive, which outperform existing approaches on real network data.

Findings

LouvainDP effectively detects communities with privacy guarantees.

ModDivisive achieves high modularity scores under differential privacy.

Both methods outperform state-of-the-art techniques in experiments.

Abstract

Complex networks usually expose community structure with groups of nodes sharing many links with the other nodes in the same group and relatively few with the nodes of the rest. This feature captures valuable information about the organization and even the evolution of the network. Over the last decade, a great number of algorithms for community detection have been proposed to deal with the increasingly complex networks. However, the problem of doing this in a private manner is rarely considered. In this paper, we solve this problem under differential privacy, a prominent privacy concept for releasing private data. We analyze the major challenges behind the problem and propose several schemes to tackle them from two perspectives: input perturbation and algorithm perturbation. We choose Louvain method as the back-end community detection for input perturbation schemes and propose the method LouvainDP which runs Louvain algorithm on a noisy super-graph. For algorithm perturbation, we design ModDivisive using exponential mechanism with the modularity as the score. We have thoroughly evaluated our techniques on real graphs of different sizes and verified their outperformance over the state-of-the-art.