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With the increasing development of mobile social networks, a large amount of data is generated by various social software. However, these datasets contain sensitive user information, and their direct release without proper privacy protection measures may lead to significant privacy breaches. Aiming at the problem of identity privacy leakage due to degree attacks on mobile social network, we proposes an privacy preserving scheme under the differential privacy model. First, we introduce a degree projection method to preserve the important nodes as much as possible. Second, we use an improved fuzzy clustering algorithm to divide the nodes, and realizes personalized differential privacy according to the variance of the node degrees in each cluster. The experiment shows that the scheme not only satisfies the differential privacy, but also has high data availability.

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Privacy-Preserving Scheme in Social Networks with Differential Privacy

  • 会议论文

  • 发表日期：

    2026年

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  • Hongyan Zhang,
  • Xinxin Zhang,
  • Li Xu

摘要

With the increasing development of mobile social networks, a large amount of data is generated by various social software. However, these datasets contain sensitive user information, and their direct release without proper privacy protection measures may lead to significant privacy breaches. Aiming at the problem of identity privacy leakage due to degree attacks on mobile social network, we proposes an privacy preserving scheme under the differential privacy model. First, we introduce a degree projection method to preserve the important nodes as much as possible. Second, we use an improved fuzzy clustering algorithm to divide the nodes, and realizes personalized differential privacy according to the variance of the node degrees in each cluster. The experiment shows that the scheme not only satisfies the differential privacy, but also has high data availability.