Author
Listed:
- 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)
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.
Suggested Citation
Jia-Wei Wang & Hai-Feng Zhang & Xiao-Jing Ma & Jing Wang & Chuang Ma & Pei-Can Zhu, 2023.
"Privacy-preserving identification of the influential nodes in networks,"
International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(10), pages 1-18, October.
Handle:
RePEc:wsi:ijmpcx:v:34:y:2023:i:10:n:s0129183123501280
DOI: 10.1142/S0129183123501280
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