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Yuanyuan WANG, Xing ZHANG, Zhiguang CHU, et al., “An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–8, xxxx doi: 10.23919/cje.2023.00.276
Citation: Yuanyuan WANG, Xing ZHANG, Zhiguang CHU, et al., “An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–8, xxxx doi: 10.23919/cje.2023.00.276

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

doi: 10.23919/cje.2023.00.276
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  • Author Bio:

    Yuanyuan WANG Female (Han ethnicity), Master's degree, native of Zhoukou City, Henan Province, currently studying Computer Science at Liaoning University of Technology. Research focus is on privacy protection of graph data. (Email: wyyuand@163.com)

    Xing ZHANG Male (Han ethnicity), received a Ph.D. degree in Computer Science and Technology from Beijing University of Technology. Currently, he is a professor and a professional member of CCF (China Computer Federation). His research focuses on network architecture and protocols, information security, and related areas. (Email: dr.zhangxing@gmail.com)

    Zhiguang CHU Male (Han ethnicity), doctoral student, senior engineer, native of Yingkou City, Liaoning Province. Research focus is on data analysis and privacy protection. (Email: chuzg@lnut.edu.cn)

    Wei SHI Female(Han ethnicity), Master's degree, laboratory technician, research focus on information security and privacy protection. (Email: shiwei@lnut.edu.cn)

    Xiang LI Male (Han ethnicity), received a Ph.D. degree in Computer and Information Science from Temple University in the United States. He is currently employed as a lecturer and his research interests include general artificial intelligence, cognitive science, and artificial intelli-gence emotions. (Email: xiangliagi@lnut.edu.cn)

  • Corresponding author: Email: dr.zhangxing@gmail.com
  • Received Date: 2023-09-28
  • Accepted Date: 2024-01-12
  • Available Online: 2024-04-01
  • 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 evaluated 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.
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