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Anonymizing Global Edge Weighted Social Network Graphs

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Security and Privacy in Social Networks and Big Data (SocialSec 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1495))

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  • The original version of this chapter was revised: The sections Introduction, Preliminary and References have been revised and updated. The correction to this chapter is available at https://doi.org/10.1007/978-981-16-7913-1_14

Abstract

Privacy protection of individual users in social networks is becoming more and more important, thus it requires effective anonymization techniques. In this paper, we use Kruskal and Prim algorithms to model the linear programming of the minimum spanning tree. Finally, we execute the experiments on the number of anonymity solutions and time with different edge weights to analyze the Kruskal algorithm and Prim algorithm to verify their anonymity feasibility.

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Change history

  • 15 November 2021

    In the originally published chapter 9 some of the paragraphs were presented in their unfinished version, which affected the readability of the paper. The sections Introduction, Preliminary and References have been revised and updated.

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Acknowledgment

The authors declare that there is no conflict of interest regarding the publication of this paper. This work was supported in part by the National Natural Science Foundation of China (No. 62171132, No. 62102088, No. U1905211, No. 61771140), Fok Ying Tung Education Foundation (No. 171061), Natural Science Foundation of Fujian Province (No. 2021J05228), and Fujian University of Technology (No. GJ-YB-20-06).

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Authors and Affiliations

  1. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350117, Fujian, People’s Republic of China

    Jiaru Wang, Ziyi Wan, Jiankang Song, Yanze Huang, Yuhang Lin & Limei Lin

  2. School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, Fujian, People’s Republic of China

    Yanze Huang

  3. Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou, 350117, Fujian, People’s Republic of China

    Ziyi Wan, Jiankang Song, Yuhang Lin & Limei Lin

Authors
  1. Jiaru Wang
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  2. Ziyi Wan
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  3. Jiankang Song
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  4. Yanze Huang
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  5. Yuhang Lin
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  6. Limei Lin
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Corresponding author

Correspondence to Limei Lin .

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Editors and Affiliations

  1. Fujian Normal University, Fuzhou, China

    Limei Lin

  2. Santa Clara University, Santa Clara, CA, USA

    Yuhong Liu

  3. National Taitung University, Taitung, Taiwan

    Chia-Wei Lee

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Wang, J., Wan, Z., Song, J., Huang, Y., Lin, Y., Lin, L. (2021). Anonymizing Global Edge Weighted Social Network Graphs. In: Lin, L., Liu, Y., Lee, CW. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2021. Communications in Computer and Information Science, vol 1495. Springer, Singapore. https://doi.org/10.1007/978-981-16-7913-1_9

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  • DOI: https://doi.org/10.1007/978-981-16-7913-1_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7912-4

  • Online ISBN: 978-981-16-7913-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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