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Greedily Remove k Links to Hide Important Individuals in Social Network

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Security and Privacy in Social Networks and Big Data (SocialSec 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1095))

Abstract

Closeness centrality is a type of measure that usually used in social network analysis (SNA). For personal privacy, we study how to help important individuals avoid being detected by closeness centrality analysis. In this paper, we present an optimization problem of finding k edges removed to minimize leader node closeness value to hide leader. We consider this problem in the undirected graph and prove its NP-completeness by reduction from the Hamiltonian cycle problem. Hence, we propose a greedy algorithm with a \((1-\frac{1}{e})\) - approximation lower bound and design UpdateCloseness algorithm to reduce time cost by Breadth-First Search algorithm. Experimental results confirm the effectivity of our greedy scheme.

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Authors and Affiliations

  1. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, Zhejiang, China

    Jie Ji, Guohua Wu, Chenjian Duan, Yizhi Ren & Zhen Wang

Authors
  1. Jie Ji
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  2. Guohua Wu
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  3. Chenjian Duan
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  4. Yizhi Ren
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  5. Zhen Wang
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Corresponding author

Correspondence to Zhen Wang .

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Editors and Affiliations

  1. Technical University of Denmark, Lyngby, Denmark

    Weizhi Meng

  2. University of Plymouth, Devon, UK

    Steven Furnell

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Ji, J., Wu, G., Duan, C., Ren, Y., Wang, Z. (2019). Greedily Remove k Links to Hide Important Individuals in Social Network. In: Meng, W., Furnell, S. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2019. Communications in Computer and Information Science, vol 1095. Springer, Singapore. https://doi.org/10.1007/978-981-15-0758-8_17

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  • DOI: https://doi.org/10.1007/978-981-15-0758-8_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0757-1

  • Online ISBN: 978-981-15-0758-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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