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

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

作者机构：School of Electronics and Information Engineering Liaoning University of Technology Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Faculty of Information Technology Beijing University of Technology 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2025年第34卷第1期

页      面：365-372页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet Protection 

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.