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A Local-Perturbation Anonymizing Approach to Preserving Community Structure in Released Social Networks

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Quality, Reliability, Security and Robustness in Heterogeneous Networks (QShine 2016)


Social networks provide a large amount of social network data, which is gathered and released for various purposes. Since social network data usually contains much sensitive information of individuals, the data needs to be anonymized before releasing. To protect privacy of individuals in released social network, many anonymizing methods have been proposed. However, most of them were proposed for general purpose, and suffered the over-information loss problem when they were used for specific purposes. In this paper, we focus on the problem of preserving structure information in anonymized social network data, which is the most important knowledge for community analysis. Furthermore, we propose a novel local-perturbation technique that can reach the same privacy requirement of k-anonymity, while minimizing the impact on community structure. We evaluate the performance of our method on real-world data. Experimental results show that our method has less community structure information loss compared with existing techniques.

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The research is supported by the National Science Foundation of China (Nos. 61272535, 61502111), Guangxi “Bagui Scholar” Teams for Innovation and Research Project, the Guangxi Collaborative Center of Multi-source Information Integration and Intelligent Processing, Guangxi Natural Science Foundation (Nos. 2013GXNSFBA019263, 2014GXNSFAA118018 and 2015GXNSFBA139246), and the Science and Technology Research Projects of Guangxi Higher Education (Nos. 2015YB032 and 2013YB029), and the Innovation Project of Guangxi Graduate Education (No. YCSZ2015104).

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Correspondence to Xianxian Li .

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, H., Liu, P., Lin, S., Li, X. (2017). A Local-Perturbation Anonymizing Approach to Preserving Community Structure in Released Social Networks. In: Lee, JH., Pack, S. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 199. Springer, Cham.

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  • Print ISBN: 978-3-319-60716-0

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