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Defend the Clique-based Attack for Data Privacy

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Combinatorial Optimization and Applications (COCOA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11346))

Abstract

Clique, as the most compact cohesive component in a graph, has been employed to identify cohesive subgroups of entities and explore the sensitive information in the online social network, crowdsourcing network, and cyber physical network, etc. In this study, we focus on the defense of clique-based attack and target at reducing the risk of entities security/privacy issues in clique structure. Since the ultimate resolution for defending the clique-based attack and risk is wrecking the clique with minimum cost, we establish the problem of clique-destroying (CD) in the network from a fundamental algorithm aspect. Interestingly, we notice that the clique-destroying problem in the directed graph is still an unsolved problem, and complexity analysis also does not exist. Therefore, we propose an innovative formal clique-destroying problem and proof the NP-complete problem complexity with solid theoretical analysis, then present effective and efficient algorithms for both undirected and directed graph. Furthermore, we show how to extend our algorithm to data privacy protection applications with controllable parameter k, which could adjust the size of a clique we wish to destroy. By comparing our algorithm with the up-to-date anonymization approaches, the real data experiment demonstrates that our resolution could efficaciously defend the clique-based security and privacy attacks.

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Notes

  1. 1.

    https://www.facebook.com/.

  2. 2.

    https://www.linkedin.com/.

  3. 3.

    https://twitter.com/.

  4. 4.

    https://www.instagram.com.

  5. 5.

    https://www.amazon.com/.

  6. 6.

    https://www.ferc.gov/.

  7. 7.

    https://www.facebook.com/apps/application.php?id=201704403232744.

  8. 8.

    http://www.epinions.com/.

  9. 9.

    https://www.wikipedia.org/.

  10. 10.

    http://cap.ece.gatech.edu/.

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Acknowledgement

This work is partly supported by the Foundation of Guizhou Provincial Key Laboratory of Public Big Data (No. 2018BDKFJJ002) and the National Science Foundation (NSF) under grant NOs. 1252292, 1741277, 1704287, and 1829674.

Author information

Authors and Affiliations

  1. Data-driven Intelligence Research (DIR) Laboratory, Kennesaw State University, Kennesaw, GA, 30060, USA

    Meng Han & Liyuan Liu

  2. Georgia State University, Atlanta, GA, 30303, USA

    Dongjing Miao

  3. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Dongjing Miao & Jinbao Wang

Authors
  1. Meng Han
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  2. Dongjing Miao
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  3. Jinbao Wang
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  4. Liyuan Liu
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Corresponding author

Correspondence to Meng Han .

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Editors and Affiliations

  1. Department of Computer Science, Kennesaw State University, Marietta, GA, USA

    Donghyun Kim

  2. Department of Mathematics and Computer Science, North Carolina Central University, Durham, NC, USA

    R. N. Uma

  3. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Alexander Zelikovsky

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Han, M., Miao, D., Wang, J., Liu, L. (2018). Defend the Clique-based Attack for Data Privacy. In: Kim, D., Uma, R., Zelikovsky, A. (eds) Combinatorial Optimization and Applications. COCOA 2018. Lecture Notes in Computer Science(), vol 11346. Springer, Cham. https://doi.org/10.1007/978-3-030-04651-4_18

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  • DOI: https://doi.org/10.1007/978-3-030-04651-4_18

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