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Privacy preservation based on clustering perturbation algorithm for social network

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Abstract

With a vigorous development of information release for social network, it is now an urgent question to protect sensitive information. The sensitive information concerned by the attackers may be usually located in a local group of large scale social networks and the operations of privacy protection are needed to minimize changes for overall structure of the network to maintain data availability. In this paper, a clustering perturbation algorithm to preserve privacy for social network was proposed considering preservation privacy of vertices properties and community structures simultaneously. The proposed algorithm introduced a strategy of exchanging attributes between vertices with same degree randomly to induce attackers to search for false targets and maintain whole structure of network. Furthermore, a perturbation strategy with tiny influences based on local clustering and modifying edges complementarily was adopted to decrease the risk of privacy disclosure considering minimum loss of network structure and data information. The experimental results showed that the proposed algorithm has more advantages over other existing state-of-the-art approaches in privacy preservation and effectiveness of social network.

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Acknowledgements

This research is supported by the financial support from Zhejiang Provincial Natural Science Foundation of China under Grant No. LY16F020027, LY16F020026, LZ15F030004 and LY15F030021, National Nature Science Foundation of China under grant No. 61703183 and 61370185 and Humanity and Social Science Youth foundation of Ministry of Education of China under Grant no. 15YJCZH088. Furthermore, the original manuscript was helped by Wenping Li and Bolin Yu

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Authors and Affiliations

  1. College of Mathematics and Information Engineering, Jiaxing University, Zhejiang, 434023, China

    Fahong Yu, Wenping Li & Huimin Gao

  2. Center of Economic Managing Experiment, Jiaxing University, Zhejiang, 434023, China

    Meijia Chen

  3. School of Electronics and Communication, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Bolin Yu

  4. Ningbo Institute of Technology, Zhejiang University, Ningbo, 315100, China

    Longhua Ma

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  1. Fahong Yu
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  2. Meijia Chen
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  3. Bolin Yu
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  4. Wenping Li
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  5. Longhua Ma
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  6. Huimin Gao
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Correspondence to Fahong Yu.

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Yu, F., Chen, M., Yu, B. et al. Privacy preservation based on clustering perturbation algorithm for social network. Multimed Tools Appl 77, 11241–11258 (2018). https://doi.org/10.1007/s11042-017-5502-3

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  • DOI: https://doi.org/10.1007/s11042-017-5502-3

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