Clustering algorithm preserving differential privacy in the framework of Spark - Zhi-qiang GAO; Qing-peng LI; Ren-yuan HU (网络与信息安全学报)
Aimed at the problem that traditional methods fail to deal with malicious attacks with arbitrary background knowledge during the process of massive data clustering analysis,an improved clustering algorithm， especially designed for preserving differential privacy,under the framework of Spark was proposed.Furthermore,it’s theoretically proved to meet the standard of ε-differential privacy in the framework of Spark platform.Finally,experimental results show that guaranteeing the availability of proposed clustering algorithm,the improved algorithm has an advantage over privacy protection and satisfaction in the aspect of time as well as efficiency.Most importantly,the proposed algorithm shows a good application prospect in the analysis of data clustering preserving privacy protection and data security.