Computational Methods and Algorithms for Multimedia Data Analysis and Security

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 654

Special Issue Editors

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Guest Editor
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China
Interests: pattern recognition; computer vision; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing, China
Interests: cryptography and security protocols; internet of things security; big data security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the proliferation of multimedia data across various platforms has led to an urgent need for effective analysis algorithms and robust data security measures. Multimedia data, including images, videos, and audio files, present unique challenges due to their diverse formats, large volumes, and complex content. Furthermore, ensuring the confidentiality, integrity, and availability of multimedia data is crucial for protecting sensitive information and maintaining user privacy. This Special Issue aims to showcase cutting-edge research in the fields of multimedia computing, data security algorithms, and optimization strategies, highlighting innovative algorithms, methodologies, and solutions to address these challenges. Within this context, the topics covered in the Special Issue encompass diverse deep learning models incorporating blockchain or repurposing. These models span various applications, including transfer learning, meta-learning, continuous learning, model fine-tuning, model retraining, model reuse, representation learning, blockchain technology, federated learning, etc.

We look forward to receiving your interesting submissions.

Topics include, but are not limited to, the following:

  • Advanced multimedia content computing and understanding techniques;
  • Deep learning and artificial intelligence for multimedia data processing;
  • Multimedia feature extraction, representation, and classification;
  • Multimedia retrieval algorithm;
  • Privacy-preserving multimedia processing and sharing;
  • Multimedia authentication and integrity verification;
  • Secure multimedia communication and streaming protocols;
  • Multimedia data protection in cloud computing and edge computing environments;
  • Person re-identification;
  • Blockchain theory and application;
  • Blockchain regulation and technology.

Dr. Keyang Cheng
Prof. Dr. Liangmin Wang
Guest Editors

Manuscript Submission Information

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  • multimedia computing
  • transfer learning
  • meta learning
  • continuous learning
  • model fine-tuning
  • model retraining
  • model reuse
  • representation learning
  • blockchain
  • federated learning

Published Papers (1 paper)

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23 pages, 4800 KiB  
Blockchain Interoperability in Data Exchange Logistics Integration
by Kaiye Li, Chun Wang, Xia Feng and Songze Wu
Mathematics 2024, 12(10), 1516; - 13 May 2024
Viewed by 346
Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting [...] Read more.
Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting the data. Additionally, when transportation information changes, downstream enterprises must promptly adjust their production plans to accommodate these alterations. The strict access controls of private blockchains can obstruct downstream enterprises from obtaining data, posing a challenge to the overall operational efficiency. In this paper, we propose an innovative logistics data protection scheme that employs private set intersection (PSI) and blockchain cross-chain technology to achieve data security. In our scheme, logistics companies within the logistics consortium are added as trusted agents to the public blockchain, enabling downstream enterprises to acquire logistics data integration from the public blockchain. Utilizing an RSA-based PSI protocol, our approach enhances exchange efficiency while protecting private data without transmitting additional information. We evaluate the performance of the proposed solution through a series of experiments, and the results demonstrate that our solution can achieve secure and efficient logistics data exchange. Full article
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