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Resisting re-identification mining on social graph data

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Abstract

Varieties of sensitive personal information become a privacy concern for social networks. However, characteristics of social graphs could be utilized by attackers to re-identify target entities of social networks. In this paper, we first analyze a new attack model named bin-based attack, which re-identifies social individuals in social networks, according to their graph structure characteristics. For bin-based attack, we propose a novel k-anonymity scheme. With this scheme, social individuals are completely k-anonymity protection. Experiments illustrate the effectiveness of the proposed scheme. The utility of anonymized networks are demonstrated with the results of vertex degree, and betweenness.

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Authors and Affiliations

  1. College of Information Science and Engineering, Central South University, Changsha, 410083, China

    Jianliang Gao & Jianxin Wang

  2. College of Computing and Informatics, Drexel University, Philladephia, 19104, USA

    Qing Ping

Authors
  1. Jianliang Gao
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  2. Qing Ping
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  3. Jianxin Wang
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Corresponding author

Correspondence to Jianxin Wang.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

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Gao, J., Ping, Q. & Wang, J. Resisting re-identification mining on social graph data. World Wide Web 21, 1759–1771 (2018). https://doi.org/10.1007/s11280-017-0524-3

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  • DOI: https://doi.org/10.1007/s11280-017-0524-3

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