Artificial Intelligence Algorithms in Information Security and Cryptography

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 61

Special Issue Editors


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Guest Editor
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: multimedia information security; information hiding; meteorological big data mining and forensics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Cyber Security, Jinan University, Guangzhou 510632, China
Interests: data classification and grading; artificial intelligence security; digital forensics

Special Issue Information

Dear Colleagues,

In the ever-evolving technological landscape, artificial intelligence (AI) has emerged as a revolutionary force, particularly within the realms of information security and cryptography. As cyber threats become increasingly sophisticated, traditional security measures are struggling to safeguard sensitive data and systems effectively. This Special Issue, entitled "Artificial Intelligence Algorithms in Information Security and Cryptography", delves into the cutting-edge integration of AI algorithms with information security and cryptographic practices. It aims to compile the latest research findings, practical applications, and theoretical foundations that underscore the synergistic relationship between AI algorithms and these two crucial fields. By doing so, it endeavors to provide a comprehensive understanding of how AI is reshaping information security and cryptography, and to inspire further innovative advancements.

Dr. Chengsheng Yuan
Prof. Dr. Zhihua Xia
Guest Editors

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Keywords

  • artificial intelligence algorithms
  • information security
  • cryptography
  • malware detection
  • cryptographic key management
  • network intrusion detection
  • cryptographic algorithm design
  • cryptographic protocol design
  • biometric security
  • privacy-preserving AI
  • forensics
  • adversarial attack
  • network situational awareness

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Published Papers (1 paper)

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Research

17 pages, 1788 KiB  
Article
Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree
by Shuai Jiang, Naoto Iwata, Sayaka Kamei, Kazi Md. Rokibul Alam and Yasuhiko Morimoto
Mathematics 2025, 13(15), 2509; https://doi.org/10.3390/math13152509 - 4 Aug 2025
Abstract
Privacy preservation poses significant challenges in third-party data sharing, particularly when handling table data containing personal information such as demographic and behavioral records. Synthetic table data generation has emerged as a promising solution to enable data analysis while mitigating privacy risks. While Generative [...] Read more.
Privacy preservation poses significant challenges in third-party data sharing, particularly when handling table data containing personal information such as demographic and behavioral records. Synthetic table data generation has emerged as a promising solution to enable data analysis while mitigating privacy risks. While Generative Adversarial Networks (GANs) are widely used for this purpose, they exhibit limitations in modeling table data due to challenges in handling mixed data types (numerical/categorical), non-Gaussian distributions, and imbalanced variables. To address these limitations, this study proposes a novel adversarial learning framework integrating gradient boosting trees for synthesizing table data, called Adversarial Gradient Boosting Decision Tree (AGBDT). Experimental evaluations on several datasets demonstrate that our method outperforms representative baseline models regarding statistical similarity and machine learning utility. Furthermore, we introduce a privacy-aware adaptation of the framework by incorporating k-anonymization constraints, effectively reducing overfitting to source data while maintaining practical usability. The results validate the balance between data utility and privacy preservation achieved by our approach. Full article
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