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Protect You More Than Blank: Anti-Learning Sensitive User Information in the Social Networks

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Abstract

Social networks are getting more and more attention in recent years. People join social networks to share their information with others. However, due to the different cultures and backgrounds, people have different requirements on what kind of information should be published. Currently, when social network websites publish data, they just leave the information that a user feels sensitive blank. This is not enough due to the existence of the label-structure relationship. A group of analyzing algorithms can be used to learn the blank information with high accuracy. In this paper, we propose a personalized model to protect private information in social networks. Specifically, we break the label-structure association by slightly changing the edges in some users’ neighborhoods. More importantly, in order to increase the usability of the published graph, we also preserve the influence value of each user during the privacy protection. We verify the effectiveness of our methods through extensive experiments. The results show that the proposed methods can protect sensitive labels against learning algorithms and at the same time, preserve certain graph utilities.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Mingxuan Yuan & Lei Chen

  2. Huawei Noah’s Ark Laboratory, Shatin, Hong Kong, China

    Mingxuan Yuan

  3. Department of Computer Science, University of Illinois at Chicago, Chicago, IL, 60637, U.S.A.

    Philip S. Yu

  4. Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, 100871, China

    Hong Mei

  5. Institute of Software, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Hong Mei

  6. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Hong Mei

Authors
  1. Mingxuan Yuan
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  2. Lei Chen
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  3. Philip S. Yu
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  4. Hong Mei
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Corresponding author

Correspondence to Mingxuan Yuan.

Additional information

This work is supported in part by the Research Grants Council (RGC) of Hong Kong, China, under Grant No. NHKUST612/09, the National Basic Research 973 Program of China under Grant No. 2012CB316200, and the National Natural Science Foundation of China under Grant No. 60931160444.

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Yuan, M., Chen, L., Yu, P.S. et al. Protect You More Than Blank: Anti-Learning Sensitive User Information in the Social Networks. J. Comput. Sci. Technol. 29, 762–776 (2014). https://doi.org/10.1007/s11390-014-1466-1

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  • DOI: https://doi.org/10.1007/s11390-014-1466-1

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