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

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

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 2895

Special Issue Editors

Prof. Dr. Ximeng Liu

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Zuobin Ying

E-Mail Website
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
Prof. Dr. Yinbin Miao

E-Mail Website
Guest Editor
School of Cyber Engineering, Xidian University, Xi'an 710071, China
Interests: data security and privacy protection; machine learning over encrypted data; blockchain
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (2 papers)

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Research

14 pages, 4275 KiB  
Article
Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
by Myung-Sun Baek and Hyoung-Kyu Song
Mathematics 2024, 12(23), 3800; https://doi.org/10.3390/math12233800 - 30 Nov 2024
Viewed by 784
Abstract
In recent years, ensuring communication security at the physical layer has become increasingly important due to the transmission of sensitive information over various networks. Traditional approaches to physical layer security often rely on artificial noise generation, which may not offer robust solutions against [...] Read more.
In recent years, ensuring communication security at the physical layer has become increasingly important due to the transmission of sensitive information over various networks. Traditional approaches to physical layer security often rely on artificial noise generation, which may not offer robust solutions against advanced interception techniques. This study addresses these limitations by proposing a novel security technique based on non-orthogonal signaling using Faster-than-Nyquist (FTN) signaling. Unlike conventional FTN methods that utilize fixed symbol intervals, the proposed technique employs variable symbol intervals encoded as secure information, shared only with legitimate receivers. This encoding enables effective interference cancellation and symbol detection at the receiver, while preventing eavesdroppers from deciphering transmitted signals. The performance of the proposed technique was evaluated using the DVB-S2X system, a practical digital video broadcasting standard. Simulation results demonstrated that the proposed method maintains smooth communication with minimal performance degradation compared to traditional methods. Furthermore, eavesdroppers were unable to decode the transmitted signals, confirming the enhanced security. This research presents a new approach to physical layer security that does not depend on generating artificial noise, offering a path to more secure and efficient communication systems. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications, 2nd Edition)
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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
Cited by 1 | Viewed by 1379
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
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications, 2nd Edition)
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