Privacy-preserving identification of the influential nodes in networks

Jia-Wei Wang, Hai-Feng Zhang, Xiao-Jing Ma, Jing Wang, Chuang Ma and Pei-Can Zhu
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Jia-Wei Wang: School of Mathematical Science, Anhui University, Hefei 230601, P. R. China
Hai-Feng Zhang: School of Mathematical Science, Anhui University, Hefei 230601, P. R. China
Xiao-Jing Ma: School of Mathematical Science, Anhui University, Hefei 230601, P. R. China
Jing Wang: School of Mathematical Science, Anhui University, Hefei 230601, P. R. China
Chuang Ma: School of Internet, Anhui University, Hefei 230601, P. R. China
Pei-Can Zhu: School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University (NWPU), Xi’an 710072, Shaanxi, P. R. China

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 10, 1-18

Abstract: Identifying influential nodes in social networks has drawn significant attention in the field of network science. However, most of the existing works request to know the complete structural information about networks, indeed, this information is usually sensitive, private and hard to obtain. Therefore, how to identify the influential nodes in networks without disclosing privacy is especially important. In this paper, we propose a privacy-preserving (named as HE-ranking) framework to identify influential nodes in networks based on homomorphic encryption (HE) protocol. The HE-ranking method collaboratively computes the nodes’ importance and protects the sensitive information of each private network by using the HE protocol. Extensive experimental results indicate that the method can effectively identify the influential nodes in the original networks than the baseline methods which only use each private network to identify influential nodes. More importantly, the HE-ranking method can protect the privacy of each private network in different parts.

Keywords: Complex network; identification of critical nodes; homomorphic encryption; privacy protection; centrality index (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123501280

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