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Security and Communication Networks
Volume 2017 (2017), Article ID 8408253, 14 pages
Research Article

An Edge Correlation Based Differentially Private Network Data Release Method

1Key Laboratory for Modern Teaching Technology, Ministry of Education, Xi’an 710062, China
2School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
3Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA

Correspondence should be addressed to Zhipeng Cai

Received 16 August 2017; Accepted 16 October 2017; Published 13 November 2017

Academic Editor: Houbing Song

Copyright © 2017 Junling Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries’ arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via Jenson-Shannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.