An Enhanced Clustering-Based(k,t)-Anonymity Algorithm for Graphs  

作　　者：Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li 

机构地区：[1]School of Electronics and Information Engineering,Liaoning University of Technology,Jinzhou 121000,China [2]Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province,Jinzhou 121000,China [3]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China

出　　处：《Chinese Journal of Electronics》2025年第1期365-372,共8页电子学报(英文版)

摘　　要：As people become increasingly reliant on the Internet,securely storing and publishing private data has become an important issue.In real life,the release of graph data can lead to privacy breaches,which is a highly challenging problem.Although current research has addressed the issue of identity disclosure,there are still two challenges:First,the privacy protection for large-scale datasets is not yet comprehensive;Second,it is difficult to simultaneously protect the privacy of nodes,edges,and attributes in social networks.To address these issues,this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering.The algorithm uses k-means++clustering for k-anonymity and t-closeness to improve k-anonymity.We evaluate the privacy and efficiency of this method on two datasets and achieved good results.This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关 键 词：Graph data Enhanced clustering K-ANONYMITY Privacy protection k-means++ t-closeness 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]