Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: 20 October 2024 | Viewed by 1156

Special Issue Editors


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Guest Editor
1. College of Computer and Data Science, Fuzhou 350000, China
2. College of Software, Fuzhou University, Fuzhou 350000, China
Interests: applied cryptography; cloud security; big data security; privacy-preserving data mining/machine learning techniques; network security
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Guest Editor
Faculty of Data Science, City University of Macau, Macau
Interests: data privacy; blockchain; privacy-preserving machine-learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Cyber Engineering, Xidian University, Xi'an 710071, China
Interests: applied cryptography; data mining; blockchain
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Special Issue Information

Dear Colleagues,

The area of intelligent multimedia involves the real-time computer processing and understanding of perceptual input from speech, textual and visual sources. It contrasts with the traditional display of text, voice, sound, and video/graphics with touch and virtual reality linked in. The benefits of intelligent multimedia include improved productivity and efficiency, better flexibility and agility, and increased profitability. It also contains many applications that can improve automation, machine-to-machine communication, manufacturing oversite, and decision making. However, despite the advantage of intelligent multimedia, it also brings many security and privacy issues such as information confidentiality, data security, and secure communication. Most of the security and privacy issues can be solved with some mathematical cryptology methods. However, the heavyweight cryptosystem still cannot be performed on various types of multimedia, which restricts the applications in intelligent multimedia applications. This Special Issue is interested in inviting and gathering recent advanced mathematical methods in intelligent multimedia computing in security and applications to address these arising challenges and opportunities differently from traditional cloud-based architectures.

Prof. Dr. Ximeng Liu
Dr. Zuobin Ying
Dr. Yinbin Miao
Guest Editors

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Keywords

  • privacy computation
  • artificial intelligence
  • data mining and knowledge discovery
  • trust and reputation
  • formal security model
  • modelling and simulation
  • performance analysis and forecasting
  • optimization and operational research

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Published Papers (1 paper)

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Research

16 pages, 821 KiB  
Article
TPoison: Data-Poisoning Attack against GNN-Based Social Trust Model
by Jiahui Zhao, Nan Jiang, Kanglu Pei, Jie Wen, Hualin Zhan and Ziang Tu
Mathematics 2024, 12(12), 1813; https://doi.org/10.3390/math12121813 - 11 Jun 2024
Viewed by 677
Abstract
In online social networks, users can vote on different trust levels for each other to indicate how much they trust their friends. Researchers have improved their ability to predict social trust relationships through a variety of methods, one of which is the graph [...] Read more.
In online social networks, users can vote on different trust levels for each other to indicate how much they trust their friends. Researchers have improved their ability to predict social trust relationships through a variety of methods, one of which is the graph neural network (GNN) method, but they have also brought the vulnerability of the GNN method into the social trust network model. We propose a data-poisoning attack method for GNN-based social trust models based on the characteristics of social trust networks. We used a two-sample test for power-law distributions of discrete data to avoid changes in the dataset being detected and used an enhanced surrogate model to generate poisoned samples. We further tested the effectiveness of our approach on three real-world datasets and compared it with two other methods. The experimental results using three datasets show that our method can effectively avoid detection. We also used three metrics to illustrate the effectiveness of our attack, and the experimental results show that our attack stayed ahead of the other two methods in all three datasets. In terms of one of our metrics, our attack method decreased the accuracies of the attacked models by 12.6%, 22.8%, and 13.8%. Full article
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