Defend the Clique-based Attack for Data Privacy

  • Meng HanEmail author
  • Dongjing Miao
  • Jinbao Wang
  • Liyuan Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11346)


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.



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.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Meng Han
    • 1
    Email author
  • Dongjing Miao
    • 2
    • 3
  • Jinbao Wang
    • 3
  • Liyuan Liu
    • 1
  1. 1.Data-driven Intelligence Research (DIR) LaboratoryKennesaw State UniversityKennesawUSA
  2. 2.Georgia State UniversityAtlantaUSA
  3. 3.Harbin Institute of TechnologyHarbinChina

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