IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v182y2024ics0960077924003837.html
   My bibliography  Save this article

Swarm intelligence for protecting sensitive identities in complex networks

Author

Listed:
  • Zhao, Jie
  • Wang, Zhen
  • Yu, Dengxiu
  • Cao, Jinde
  • Cheong, Kang Hao

Abstract

The growing concern about data security in complex networks is a topic of immense interest. In this paper, we study how community deception can prevent malicious attackers from stealing confidential and private information from users. Community deception aims to tackle problems generated by community detection algorithms by rewiring the edges minimally. Finding the optimal edge set is essential in community deception, but this is a challenging problem coupled with high complexity. To address this issue, we propose the Swarm Community Protector (SCP), a swarm intelligence-based privacy-preserving model in terms of community structure. The SCP is designed to be adapted for graphs, and specifically edges, as opposed to the classical swarm algorithms, which are suitable for continuous and discrete domains only. We also develop an adaptive mechanism to ensure global and local searchability. A novel fitness function based on structure entropy is proposed to promote the obfuscation of community structure, thereby hiding the membership of sensitive entities in networks. Furthermore, we customize the solution space and integrate a pre-processing mechanism to reduce the search space. We have tested SCP on various real-world networks, and the experimental results indicate that SCP can provide excellent protection to the targeted community.

Suggested Citation

  • Zhao, Jie & Wang, Zhen & Yu, Dengxiu & Cao, Jinde & Cheong, Kang Hao, 2024. "Swarm intelligence for protecting sensitive identities in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:chsofr:v:182:y:2024:i:c:s0960077924003837
    DOI: 10.1016/j.chaos.2024.114831
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924003837
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2024.114831?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    2. Marcin Waniek & Tomasz P. Michalak & Michael J. Wooldridge & Talal Rahwan, 2018. "Hiding individuals and communities in a social network," Nature Human Behaviour, Nature, vol. 2(2), pages 139-147, February.
    3. Mishra, Ankit & Wen, Tao & Cheong, Kang Hao, 2024. "Efficient traffic management in networks with limited resources: The switching routing strategy," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    5. Bozkurt, Fatma & Yousef, Ali & Abdeljawad, Thabet & Kalinli, Adem & Mdallal, Qasem Al, 2021. "A fractional-order model of COVID-19 considering the fear effect of the media and social networks on the community," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    6. Li, Xianghua & Zhen, Xiyuan & Qi, Xin & Han, Huichun & Zhang, Long & Han, Zhen, 2023. "Dynamic community detection based on graph convolutional networks and contrastive learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    7. Contreras-Aso, Gonzalo & Criado, Regino & Vera de Salas, Guillermo & Yang, Jinling, 2023. "Detecting communities in higher-order networks by using their derivative graphs," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    8. Lai, Joel Weijia & Cheong, Kang Hao, 2024. "A Parrondo paradoxical interplay of reciprocity and reputation in social dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Bloch, Francis & Dutta, Bhaskar & Dziubiński, Marcin, 2020. "A game of hide and seek in networks," Journal of Economic Theory, Elsevier, vol. 190(C).
    3. Chaharborj, Sarkhosh Seddighi & Nabi, Khondoker Nazmoon & Feng, Koo Lee & Chaharborj, Shahriar Seddighi & Phang, Pei See, 2022. "Controlling COVID-19 transmission with isolation of influential nodes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    4. Wu, Yali & Dong, Ang & Ren, Yuanguang & Jiang, Qiaoyong, 2023. "Identify influential nodes in complex networks: A k-orders entropy-based method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    5. Liu, Jia-Bao & Zheng, Ya-Qian & Lee, Chien-Chiang, 2024. "Statistical analysis of the regional air quality index of Yangtze River Delta based on complex network theory," Applied Energy, Elsevier, vol. 357(C).
    6. Alharbi, Njud S. & Bekiros, Stelios & Jahanshahi, Hadi & Mou, Jun & Yao, Qijia, 2024. "Spatiotemporal wavelet-domain neuroimaging of chaotic EEG seizure signals in epilepsy diagnosis and prognosis with the use of graph convolutional LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    7. Tao, Li & Kong, Shengzhou & He, Langzhou & Zhang, Fan & Li, Xianghua & Jia, Tao & Han, Zhen, 2022. "A sequential-path tree-based centrality for identifying influential spreaders in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    8. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    9. Hajarathaiah, Koduru & Enduri, Murali Krishna & Anamalamudi, Satish, 2022. "Efficient algorithm for finding the influential nodes using local relative change of average shortest path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    10. Cao, Huiying & Gao, Chao & Wang, Zhen, 2023. "Ranking academic institutions by means of institution–publication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    11. Wang, Ying & Zheng, Yunan & Shi, Xuelei & Liu, Yiguang, 2022. "An effective heuristic clustering algorithm for mining multiple critical nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    12. Wang, Feifei & Sun, Zejun & Gan, Quan & Fan, Aiwan & Shi, Hesheng & Hu, Haifeng, 2022. "Influential node identification by aggregating local structure information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    13. Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    14. Tian, Yang & Tian, Hui & Cui, Qimei & Zhu, Xuzhen, 2024. "Phase transition phenomena in social propagation with dynamic fashion tendency and individual contact," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    15. Yin, Rongrong & Li, Linhui & Wang, Yumeng & Lang, Chun & Hao, Zhenyang & Zhang, Le, 2024. "Identifying critical nodes in complex networks based on distance Laplacian energy," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    16. Li, Jie & Wang, Ying & Zhong, Jilong & Sun, Yun & Guo, Zhijun & Chen, Zhiwei & Fu, Chaoqi, 2022. "Network resilience assessment and reinforcement strategy against cascading failure," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    17. Wang, Longjian & Zheng, Shaoya & Wang, Yonggang & Wang, Longfei, 2021. "Identification of critical nodes in multimodal transportation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    18. Jin, Pengfei & Wang, Saige & Meng, Zheng & Chen, Bin, 2023. "China's lithium supply chains: Network evolution and resilience assessment," Resources Policy, Elsevier, vol. 87(PB).
    19. Wang, Yan & Zhang, Ling & Yang, Junwen & Yan, Ming & Li, Haozhan, 2024. "Multi-factor information matrix: A directed weighted method to identify influential nodes in social networks," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    20. Feipeng Guo & Zifan Wang & Shaobo Ji & Qibei Lu, 2022. "Influential Nodes Identification in the Air Pollution Spatial Correlation Weighted Networks and Collaborative Governance: Taking China’s Three Urban Agglomerations as Examples," IJERPH, MDPI, vol. 19(8), pages 1-17, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:182:y:2024:i:c:s0960077924003837. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.