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Journal of Combinatorial Optimization

Erschienen in:

30.01.2020

A balm: defend the clique-based attack from a fundamental aspect

verfasst von: Meng Han, Dongjing Miao, Jinbao Wang, Liyuan Liu

Erschienen in: Journal of Combinatorial Optimization | Ausgabe 3/2022

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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 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.

Vorheriger Artikel On the approximability of Time Disjoint Walks

Nächster Artikel Isolation branching: a branch and bound algorithm for the k-terminal cut problem

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Titel: A balm: defend the clique-based attack from a fundamental aspect
verfasst von: Meng Han
Dongjing Miao
Jinbao Wang
Liyuan Liu
Publikationsdatum: 30.01.2020
Verlag: Springer US
Erschienen in: Journal of Combinatorial Optimization / Ausgabe 3/2022
Print ISSN: 1382-6905
Elektronische ISSN: 1573-2886
DOI: https://doi.org/10.1007/s10878-020-00527-x

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