PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information

TL;DR

PrivGraph is a novel method for privately publishing graph data by leveraging community structures, reducing noise and information loss compared to existing approaches.

Contribution

It introduces a community-aware graph synthesis algorithm that differentially privately partitions and reconstructs graphs, improving privacy-utility trade-offs.

Findings

Effective on six real-world datasets

Outperforms existing methods in graph metrics

Reduces noise and information loss

Abstract

Graph data is used in a wide range of applications, while analyzing graph data without protection is prone to privacy breach risks. To mitigate the privacy risks, we resort to the standard technique of differential privacy to publish a synthetic graph. However, existing differentially private graph synthesis approaches either introduce excessive noise by directly perturbing the adjacency matrix, or suffer significant information loss during the graph encoding process. In this paper, we propose an effective graph synthesis algorithm PrivGraph by exploiting the community information. Concretely, PrivGraph differentially privately partitions the private graph into communities, extracts intra-community and inter-community information, and reconstructs the graph from the extracted graph information. We validate the effectiveness of PrivGraph on six real-world graph datasets and seven commonly used graph metrics.