Differentially Private Release of the Distribution of Clustering Coefficients across Communities

作者：Li, Xiaoye; Yang, Jing^*; Sun, Zhenlong; Zhang, Jianpei

来源：Security and Communication Networks, 2019, 2019: UNSP 2518714.

DOI：10.1155/2019/2518714

• Absolute gain
• Between clusters
• Clustering coefficient
• Differential privacies
• Real-world datasets

Aiming to provide more information about the behaviors between groups or patterns between clusters in social networks, we propose a two-step differentially private method to release the distribution of clustering coefficients across communities. The DPLM algorithm improves a Louvain method to partition one network using an exponential mechanism. We introduce an absolute gain of modularity to sanitize neighboring communities. Otherwise, the algorithm is difficult to converge due to the randomness introduced. The DPCC algorithm charts the noisy distribution of clustering coefficients as a histogram, which presents the results in an intuitive manner. We conduct experiments on three real-world datasets to evaluate the proposed method. The experimental results indicate that the proposed method provides valuable distribution results while guaranteeing epsilon-differential privacy. Moreover, the DPLM algorithm can obtain better modularity for the networks.

• 出版日期2019
• 单位齐齐哈尔大学; 哈尔滨工程大学