基于云计算的社交网络安全隐私数据融合方法  

Security and Privacy Data Fusion Method for Social Network Based on Cloud Computing

作　　者：傅江辉[1] FU Jianghui(Jiangxi Engineering Laboratory of Radioactive Geoscience and Big Data Technology,East China University of Technology,Nanchang 330013,Jiangxi,China)

机构地区：[1]东华理工大学江西省放射性地学大数据技术工程实验室,江西南昌330013

出　　处：《济南大学学报（自然科学版）》2021年第1期29-33,共5页Journal of University of Jinan(Science and Technology)

基　　金：国家自然科学基金项目(61866025);江西省自然科学基金项目(20181BCB24008N)。

摘　　要：针对现有数据融合方法存在融合精度低、数据完整性差等问题,提出基于云计算的社交网络安全隐私数据融合方法;首先利用敏感信息过滤模型进行社交网络参与者个人隐私数据过滤,然后通过k匿名技术对过滤出的隐私数据进行保护处理,利用k均值聚类算法对个人隐私数据进行聚类,最后使用决策树构建数据融合模型,实现数据融合。仿真测试结果表明:与基于加权平均法、卡尔曼滤波法、Dempster-Shafer证据推理的3种数据融合方法相比,所提出方法的融合精度和数据完整性都得到了优化。In view of problems of insufficient fusion precision and poor data integrity of existing data fusion methods,a security and privacy data fusion method for social network based on cloud computing was proposed.The sensitive infor-mation filtering model was firstly used to filter personal privacy data of participants in social network,then the filtered privacy data was protected by using k-anonymity technology.k-means clustering algorithm was used for personal privacy data clustering.The decision tree-based data fusion model was finally established to realize data fusion.The simulation results show that the fusion accuracy and data integrity of the proposed method are optimized compared with three data fusion methods based on weighted average method,Kalman filtering method,and Dempster-Shafer evidential reasoning.

关 键 词：社交网络安全 数据融合 云计算 隐私数据 k匿名技术 

分 类 号：TP522[自动化与计算机技术]