Open Access
ARTICLE
An Iteration-Based Differentially Private Social Network Data Release
Tianqing Zhu1, Mengmeng Yang1, Ping Xiong2,
Yang Xiang1, Wanlei Zhou1
1 School of Information Techonolgy, Deakin University, Burwood, Australia
E-mail: t.zhu@deakin.edu.au, ymengm@deakin.edu.au, yang.xiang@deakin.edu.au, wanlei.zhou@deakin.edu.au
2 School of Information and Security Engineering, Zhongnan University of Economics and Law, Wuhan, China
E-mail: pingxiong@znufe.edu.cn
Computer Systems Science and Engineering 2018, 33(2), 61-69. https://doi.org/10.32604/csse.2018.33.061
Abstract
Online social networks provide an unprecedented opportunity for researchers to analysis various social phenomena. These network data is normally
represented as graphs, which contain many sensitive individual information. Publish these graph data will violate users’ privacy. Differential privacy is
one of the most influential privacy models that provides a rigorous privacy guarantee for data release. However, existing works on graph data publishing
cannot provide accurate results when releasing a large number of queries. In this paper, we propose a graph update method transferring the query release
problem to an iteration process, in which a large set of queries are used as update criteria. Compared with existing works, the proposed method enhances
the accuracy of query results. The extensive experiment proves that the proposed solution outperforms two state-of-the-art methods, the Laplace method
and the correlated method, in terms of Mean Absolute Value. It means our methods can retain more utility of the queries while preserving the privacy.
Cite This Article
T. Zhu, M. Yang, P. Xiong, Y. Xiang and W. Zhou, "An iteration-based differentially private social network data release,"
Computer Systems Science and Engineering, vol. 33, no.2, pp. 61–69, 2018. https://doi.org/10.32604/csse.2018.33.061
Citations