ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (6): 1244-1251.doi: 10.7544/issn1000-1239.2019.20180306

• 信息安全 • 上一篇    下一篇



  1. 1(中国科学院计算技术研究所 北京 100190);2(中国科学院大学 北京 100049);3(澳门科技大学 澳门 519020) (
  • 出版日期: 2019-06-01
  • 基金资助: 

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).

摘要: 链路预测(link prediction)是社交网络中社交关系预测和推荐的重要手段,然而链路预测过程中需要大量用户个人信息,带来了极大的隐私泄露的危险.用户很可能拒绝提供链路预测需要的信息,这将导致链路预测效果的下降,从而会进一步伤害用户体验.为了打消用户隐私泄露的顾虑,激励用户为链路预测提供更多的数据,提出了一种社交网络链路预测的个性化隐私保护方法.摆脱了对服务商的完全依赖,让用户和服务商共同合作来完成链路预测;为敏感信息和非敏感信息添加不同强度的噪声干扰,保护敏感链路不被泄露的同时维持较好的链路预测效果;并根据用户个性化的隐私设置,保证用户的敏感链路不会被公开的非敏感链路反推.最后,理论证明了提出的方法可以满足ε-差分隐私,并在真实数据集上验证了PrivLP能够在维持较高的链路预测准确性的前提下有效提升隐私保护效果.

关键词: 链路预测, 社交网络, 隐私保护, 个性化, 差分隐私

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