Community Preserved Social Graph Publishing with Node Differential Privacy
Sen Zhang, Weiwei Ni, Nan Fu

TL;DR
This paper introduces PrivCom, a node-differential privacy algorithm for social graph publishing that preserves community structure by using a Katz index-based feature extraction and a private eigen-decomposition method.
Contribution
It proposes a novel node-DP graph publishing approach that enhances community preservation through a Katz index-based feature extraction and a private eigen-decomposition technique.
Findings
PrivCom effectively preserves community structures in social graphs.
The method achieves higher utility compared to edge-DP approaches.
Experimental results validate the theoretical advantages of PrivCom.
Abstract
The goal of privacy-preserving social graph publishing is to protect individual privacy while preserving data utility. Community structure, which is an important global pattern of nodes, is a crucial data utility as it serves as fundamental operations for many graph analysis tasks. Yet, most existing methods with differential privacy (DP) commonly fall in edge-DP to sacrifice security in exchange for utility. Moreover, they reconstruct graphs from the local feature-extraction of nodes, resulting in poor community preservation. Motivated by this, we propose PrivCom, a strict node-DP graph publishing algorithm to maximize the utility on the community structure while maintaining a higher level of privacy. Specifically, to reduce the huge sensitivity, we devise a Katz index-based private graph feature extraction method, which can capture global graph structure features while greatly…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Mobile Crowdsensing and Crowdsourcing · Privacy, Security, and Data Protection
