
Research Article
Social Internet of Tings Trust Management Based on Implicit Social Relationship
@INPROCEEDINGS{10.1007/978-3-031-30623-5_9, author={Hongbin Zhang and Fan Fan and Dongmei Zhao and Bin Liu and Yanxia Wang and Jian Liu}, title={Social Internet of Tings Trust Management Based on Implicit Social Relationship}, proceedings={Security and Privacy in New Computing Environments. 5th EAI International Conference, SPNCE 2022, Xi’an, China, December 30-31, 2022, Proceedings}, proceedings_a={SPNCE}, year={2023}, month={4}, keywords={Social Internet of Things Trust Management Malicious Attack Implicit Social Relationship Multiple Social Relationships}, doi={10.1007/978-3-031-30623-5_9} }
- Hongbin Zhang
Fan Fan
Dongmei Zhao
Bin Liu
Yanxia Wang
Jian Liu
Year: 2023
Social Internet of Tings Trust Management Based on Implicit Social Relationship
SPNCE
Springer
DOI: 10.1007/978-3-031-30623-5_9
Abstract
The “Social Internet of Things (SIoT)” is a combination of the Internet of Things (IoT) and social networks to form a new paradigm. The SIoT promotes the development of smart cities, smart transportation, and many other fields. In SIoT, the openness and mobility of objects are enhanced. However, this tends to lead to network data sparsity problems. By distinguishing explicit and implicit social relationships, we introduce an implicit social relationship-based trust management model (IRTM) for reliable service delivery in SIoT. IRTM establishes implicit social relationships among nodes by mining their latent characteristics and trust transitivity. It models SIoT by creating sub-networks for each social relationship as a way to fuse the impact of different types of social relationships on trust management. To address the problem of malicious attacks by malicious nodes in the network to protect their interests, it considers two metrics, node relationship strength, and recommendation reliability, to filter malicious recommendations. Experiments conducted in the presence of data sparsity and malicious objects show that IRTM can improve the accuracy and convergence of trust evaluation compared to other methods that ignore implicit social relationships when computing trust. In addition, our scheme can improve resistance to trust-related attacks.