文献类型：会议论文

英文题名：A Two-Phase Algorithm for Generating Synthetic Graph Under Local Differential Privacy

作者：Zhang, Yuxuan[1];Wei, Jianghong[1];Zhang, Xiaojian[2];Hu, Xuexian[1];Liu, Wenfen[3]

第一作者：Zhang, Yuxuan

通讯作者：Zhang, YX[1]

机构：[1]State Key Lab Math Engn & Adv Comp, Zhengzhou 450001, Henan, Peoples R China;[2]Henan Univ Econ & Law, Coll Comp & Informat Engn, Zhengzhou 450002, Henan, Peoples R China;[3]Guangxi Key Lab Cryptog & Informat Secur, Guilin 541004, Guangxi, Peoples R China

第一机构：State Key Lab Math Engn & Adv Comp, Zhengzhou 450001, Henan, Peoples R China

通讯机构：[1]corresponding author), State Key Lab Math Engn & Adv Comp, Zhengzhou 450001, Henan, Peoples R China.

会议论文集：8th International Conference on Communication and Network Security (ICCNS)

会议日期：NOV 02-04, 2018

会议地点：Qingdao, PEOPLES R CHINA

语种：英文

外文关键词：differential privacy; synthetic graph generation; random response; local differential privacy; graph publishing

摘要：With the rapid development of big data technology, the issue of preserving personal privacy has attracted more and more attention. It has been shown that protecting the published graph with the differential privacy can ensure not only the quantitative privacy, but also the data usability. In this paper, we first put forward an optimized randomized response algorithm for generating synthetic graph, which dose not depend on a trusted third party in charge of collecting data. Furthermore, based on multi-party computation clustering, we propose a generated graph model under local differential privacy (LDPGM). The experiment indicates that LDPGM can effectively control the density of the synthetic graph so that significantly reduce the error between the synthetic graph and the original graph. Therefore, it maintains the properties of the original graph well and ensures high usability.