PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information
Quan Yuan, Zhikun Zhang, Linkang Du, Min Chen, Peng Cheng, Mingyang, Sun

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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Data Quality and Management · Privacy, Security, and Data Protection
