Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee

TL;DR

This paper introduces a new differentially private method for generating synthetic attributed social graphs that accurately preserve community structures and global properties, improving privacy-preserving data sharing.

Contribution

The paper proposes C-AGM, a novel community-preserving generative model with private sampling and estimation methods for attributed graphs, enhancing privacy and structural fidelity.

Findings

Outperforms existing methods in preserving community structure.

Maintains degree sequences and clustering coefficients effectively.

Provides strong formal differential privacy guarantees.

Abstract

We present a novel method for publishing differentially private synthetic attributed graphs. Unlike preceding approaches, our method is able to preserve the community structure of the original graph without sacrificing the ability to capture global structural properties. Our proposal relies on C-AGM, a new community-preserving generative model for attributed graphs. We equip C-AGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release community-preserving synthetic attributed social graphs with a strong formal privacy guarantee. Through comprehensive experiments, we show that our new model outperforms its most relevant counterparts in synthesising differentially private attributed social graphs that preserve the community structure of the original graph, as well as degree sequences and clustering coefficients.