ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (6): 1244-1251.doi: 10.7544/issn1000-1239.2019.20180306

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Personalized Privacy Preserving Link Prediction in Social Networks

Meng Xuying1,2, Zhang Qijia1,2, Zhang Hanwen1,2, Zhang Yujun1,2, Zhao Qinglin3   

  1. 1(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190);2(University of Chinese Academy of Sciences, Beijing 100049) 3 (Macau University of Science and Technology, Macau 519020)
  • Online:2019-06-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61672500, 61572474, 61872452, 61872451), the International S&T Cooperation Program of China (2016YFE0121500), and the FDCT-MOST Projects (001/2015/AMJ).

Abstract: Link prediction is widely used to predict and recommend social relationships in social networks. However, it requires users’ personal information, leading to great risks to users’ privacy. To prevent privacy leakage, users may refuse to provide needed information to the service provider, which in turn brings in decreases on the effectiveness of link prediction, and further hurts user experience. To eliminate the concerns of privacy disclosure and encourage users to provide more data for link prediction, we propose personalized privacy preserving link prediction in social network. We get rid of the full dependence on the service provider and friends by making users and the service provider cooperate to complete the process of link prediction. Also, we attach different magnitude noise with personalized privacy settings, maintaining the effectiveness of link prediction while protecting sensitive links and sensitive attributes. Finally, theoretical analysis is provided based on differential privacy, and experimental results on real world datasets show that our proposed methods can provide better privacy protection while maintaining the effectiveness of link prediction.

Key words: link prediction, social network, privacy protection, personalized, differential privacy

CLC Number: