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Publishing Social Graphs with Differential Privacy Guarantees Based on wPINQ

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To publish social graphs with differential privacy guarantees for reproducing valuable results of scientific researches, we study a workflow for graph synthesis and propose an improved approach based on weighted Privacy integrated query (wPINQ). The workflow starts with a seed graph to fit the noisy degree sequence, which essentially is the 1K-graph. In view of the inaccurate assortativity coefficient, we truncate the workflow to replace the seed graph with an optimal one by doing target 1K-rewiring while preserving the 1K-distribution. Subsequently, Markov chain Monte Carlo employs the new seed graph as the initial state, and proceeds step by step guided by the information of Triangles by intersect to increase the number of triangles in the synthetic graphs. The experimental results show that the proposed algorithm achieves better performance for the published social graphs.

Differential privacySocial networksweighted Privacy integrated query (wPINQ)dK-graph model

LI Xiaoye、YANG Jing、SUN Zhenlong、ZHANG Jianpei

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College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China

College of Computer and Control Enginewaeering, Qiqihar University, Qiqihar 161006, China

This work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaNatural Science Foundation of Heilongjiang ProvinceYouth Science Fund of Heilongjiang ProvinceYouth Science Fund of Heilongjiang ProvincePostdoctoral Fellowship of Heilongjiang ProvinceFundamental Research Funds in Heilongjiang Provincial UniversitiesFundamental Research Funds in Heilongjiang Provincial Universities

61672179No.61370083No.61402126F2015030QC2016083No.QC2017079LBH-Z14071135109245No.135109314

2019

10.1049/cje.2018.12.003

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(2)
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