Quality, Reliability, Security and Robustness in Heterogeneous Networks. 12th International Conference, QShine 2016, Seoul, Korea, July 7–8, 2016, Proceedings

Research Article

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

Download
266 downloads
  • @INPROCEEDINGS{10.1007/978-3-319-60717-7_4,
        author={Huanjie Wang and Peng Liu and Shan Lin and Xianxian Li},
        title={A Local-Perturbation Anonymizing Approach to Preserving Community Structure in Released Social Networks},
        proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Networks. 12th International Conference, QShine 2016, Seoul, Korea, July 7--8, 2016, Proceedings},
        proceedings_a={QSHINE},
        year={2017},
        month={8},
        keywords={Social networks Privacy protection Community structure},
        doi={10.1007/978-3-319-60717-7_4}
    }
    
  • Huanjie Wang
    Peng Liu
    Shan Lin
    Xianxian Li
    Year: 2017
    A Local-Perturbation Anonymizing Approach to Preserving Community Structure in Released Social Networks
    QSHINE
    Springer
    DOI: 10.1007/978-3-319-60717-7_4
Huanjie Wang1,*, Peng Liu1,*, Shan Lin1,*, Xianxian Li1,*
  • 1: Guangxi Normal University
*Contact email: whj.6040@163.com, liupeng@gxnu.edu.cn, lin-sam@foxmail.com, lixx@gxnu.ede.cn

Abstract

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 -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.