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

Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee AbstractWe present a novel method for publishing differentially private synthetic attributed graphs. Our method allows, for the first time, to publish synthetic graphs simultaneously preserving structural properties, user attributes and the community structure of the original graph. Our proposal relies on CAGM, a new community-preserving generative model for attributed graphs. We equip CAGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release communitypreserving 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings on Privacy Enhancing Technologies de Gruyter

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

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References (77)

Publisher
de Gruyter
Copyright
© 2020 Xihui Chen et al., published by Sciendo
ISSN
2299-0984
eISSN
2299-0984
DOI
10.2478/popets-2020-0066
Publisher site
See Article on Publisher Site

Abstract

AbstractWe present a novel method for publishing differentially private synthetic attributed graphs. Our method allows, for the first time, to publish synthetic graphs simultaneously preserving structural properties, user attributes and the community structure of the original graph. Our proposal relies on CAGM, a new community-preserving generative model for attributed graphs. We equip CAGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release communitypreserving 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.

Journal

Proceedings on Privacy Enhancing Technologiesde Gruyter

Published: Oct 1, 2020

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