HE-SNA: an efficient cross-platform network alignment scheme from privacy-aware perspective

User alignment across online social network platforms (OSNPs) is a growing concern with the rapid development of internet technology. In reality, users tend to register different accounts on multiple OSNPs, and the network platforms are reluctant to share network structure and user’s information due...

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Bibliographic Details
Published in:Complex & intelligent systems 2023-10, Vol.9 (5), p.6009-6022
Main Authors: Zhou, Li, Ma, Xiao-Jing, Pan, Dong-Hui, Fan, Dong-Mei, Zhang, Hai-Feng, Zhong, Kai
Format: Article
Language:English
Subjects:
Algorithms
Alignment
Complexity
Computational Intelligence
Cross-platform network alignment
Data Structures and Information Theory
Encryption
Engineering
Homomorphic encryption
Original Article
Privacy
Privacy-preserving
Private sub-network
Servers
Social networks
Third party
Topology
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Summary:User alignment across online social network platforms (OSNPs) is a growing concern with the rapid development of internet technology. In reality, users tend to register different accounts on multiple OSNPs, and the network platforms are reluctant to share network structure and user’s information due to business interest and privacy protection, which brings great obstacles to cross-platform user alignment. In view of this, we propose a homomorphic encryption-based social network alignment (HE-SNA) algorithm from the perspective of privacy leakage. Specifically, we first consider the OSNPs as a system containing multiple social networks, that each participant of OSNPs owns part of the network, i.e., a separate private sub-network. Then, encryption, fusion and decryption operations of the alignment information are performed by two third-party servers using HE scheme, which can protect the privacy information of sub-networks effectively. Finally, each sub-network uses the fused alignment information sent back from the third-party server for user alignment. Experimental results show that the HE-SNA method can provide a sum of locally trained models to third-party servers without leaking the privacy of any single sub-network. Moreover, the HE-SNA achieves a promising network alignment performance than only using the structural information and alignment data of single private sub-network while protecting its topology structure information.
ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-023-01052-0