Differentially Private Generation of Social Networks via Exponential Random Graph Models
Differentially Private Generation of Social Networks via Exponential Random Graph Models
复制标题
通过指数随机图模型的社交网络的差分隐私生成
DOI：
10.1109/compsac48688.2020.00-11
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Bowen, Claire
中科院分区：
文献类型：
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作者：
Liu, Fang;Eugenio, Evercita;Jin, Ick Hoon;Bowen, Claire
许多社交网络包含敏感的关系信息。保护敏感关系信息同时为社交网络研究和分析提供灵活性的一种方法是在给定原始观察到的网络的情况下，以预先指定的隐私风险水平释放合成社交网络。我们提出了DP-ERGM过程，通过指数随机图模型（EGRM）合成满足差分隐私（DP）的网络。将DP-ERGM应用于一个大学生友谊网络，并将其在生成的私有网络中的原始网络信息保存与另外两种方法进行了比较：差分私有DyadWise随机响应（DWRR）和边给定属性类的条件概率的消毒（SCEA）。结果表明，DP-EGRM保留原始信息显着优于DWRR和SCEA在网络统计和推理ERGMs和潜在空间模型。此外，DP-ERGM满足节点DP，这是比DWRR和SCEA满足的边缘DP更强的隐私概念。
Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pre-specified privacy risk level, given the original observed network. We propose the DP-ERGM procedure that synthesizes networks that satisfy the differential privacy (DP) via the exponential random graph model (EGRM). We apply DP-ERGM to a college student friendship network and compare its original network information preservation in the generated private networks with two other approaches: differentially private DyadWise Randomized Response (DWRR) and Sanitization of the Conditional probability of Edge given Attribute classes (SCEA). The results suggest that DP-EGRM preserves the original information significantly better than DWRR and SCEA in both network statistics and inferences from ERGMs and latent space models. In addition, DP-ERGM satisfies the node DP, a stronger notion of privacy than the edge DP that DWRR and SCEA satisfy.