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Data Security Sharing Method of Opportunistic Network Routing Nodes Based on Knowledge Graph and Big Data

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Multimedia Technology and Enhanced Learning (ICMTEL 2023)

Abstract

Opportunity network is one of the main network types for data sharing applications today. Due to the completely self-organized and distributed characteristics of its own structure, there is a greater security risk in the process of data sharing. Therefore, an opportunity based on knowledge graph and big data is proposed. Research on data security sharing method of network routing nodes. Analyze the opportunistic network routing protocol, expound the data sharing mode of the opportunistic network routing nodes, represent the opportunistic network based on the knowledge graph, calculate the influence degree parameter of the routing node, build the routing node influence propagation model based on this, formulate the routing node data publishing/subscribing rules, combined with Secure multi-party computing big data builds a routing node data security sharing architecture to realize the secure sharing of opportunistic network routing node data. The experimental results show that after the method is applied, the minimum value of the shared data packet loss rate reaches 4%, and the maximum value of the data sharing safety factor reaches 0.98, which fully confirms that the method has a better data security sharing effect.

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Correspondence to Yan Zhao .

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wan, X., Zhao, Y. (2024). Data Security Sharing Method of Opportunistic Network Routing Nodes Based on Knowledge Graph and Big Data. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-031-50577-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-50577-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50576-8

  • Online ISBN: 978-3-031-50577-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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