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基于边差分隐私的社交网络图结构数据发布方法
首发时间：2024-07-24
摘要：社交网络中攻击者可以通过其结构特征推断出个体隐私，从而获取和公开用户的关系网络和交易记录等潜在敏感信息，而当前研究数据可用性不足、忽略节点全局重要性、噪声未能灵活地添加到不同阶段。基于此，本文提出了一种基于边差分隐私的社交网络图数据发布方法，迭代更新加噪katz中心性，节点分组实施边差分隐私扰动，应用随机响应机制调整节点邻居关系。实验表明，在不同规模数据集中，本方法信息丢失更少，数据效用更高。最终，本文实现了在边差分隐私约束下，最大限度发布真实社交网络图的目标。
关键词： 计算机系统结构 社交网络 边差分隐私 katz中心性
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social network graph structure data publishing method based on edge differential privacy
abstract：in social networks, attackers can infer individual privacy through its structural characteristics, so as to obtain and disclose potentially sensitive information such as user relationship networks and transaction records. however, current research lacks data availability, ignores the global importance of nodes, and fails to flexibly add noise to different stages. based on this, we propose a social network graph data publishing method based on edge differential privacy. we iteratively update the noisy katz centrality, group the nodes to implement edge differential privacy perturbation, and use a random response mechanism to adjust the node neighbor relationship. experiments show that we have less information loss and higher data utility in different scale data sets. finally, we achieve the goal of maximally releasing the real social network graph under the constraint of edge differential privacy.
keywords： computer system architecture social network edge differencial privacy katz centrality
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基于边差分隐私的社交网络图结构数据发布方法
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