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Mathematics
Special Issues
Mathematical Models in Information Security and Cryptography
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Mathematical Models 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: 21 July 2025 | Viewed by 2590

Special Issue Editors

Prof. Dr. Jiangang Shu

E-Mail Website
Guest Editor
School of Cyberspace Security/Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Interests: applied cryptography; data security and privacy-preserving; AI security; cloud computing security; crowdsourcing security; network security
Prof. Dr. Yong Ding

E-Mail Website
Guest Editor
Department of Computer and Information Security, Guangdong University of Science and Technology, Dongguan 523083, China
Interests: cryptography; mathematics; information security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haining Yu

E-Mail Website
Guest Editor
School of Cyberspace Science, Harbin Institute of Technology, Harbin 150006, China
Interests: data security; privacy computing and AI security

Special Issue Information

Dear Colleagues,

In the rapidly evolving digital world, the importance of information security and cryptography cannot be overstated. The utilization of mathematical models in this domain plays a pivotal role in enhancing data protection, ensuring secure communication channels and thwarting cyber attacks. Mathematical models provide a rigorous framework for analyzing cryptographic algorithms, assessing vulnerabilities, and quantifying the degree of security. Moreover, the integration of mathematical models ensures cryptographic systems to hold desired properties such as confidentiality, authenticity, and non-repudiation. This special issue aims to explore innovative methodologies, fundamental theories and practical applications that tackle the evoling challenges in security and cryptography, offering new insights into how mathematical models can be applied to enhance the security and resilience of system.

The scope of this special issue encompasses a wide range of topics related to mathematical models in information security and cryptography. Research areas may include (but not limited to) the following:

  • Mathematical foundations of cryptography
  • Cryptographic protocols and algorithms
  • Secure multiparty computation
  • Post-quantum cryptography
  • Cryptanalysis and attacks
  • Privacy-enhancing technologies
  • Authentication and anonymity protocols
  • Security and privacy in federated learning, crowdourscing and cloud computing
  • Security considerations in emerging technologies such as IoT and blockchain

Prof. Dr. Jiangang Shu
Prof. Dr. Yong Ding
Prof. Dr. Haining Yu
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

  • information security
  • cryptography
  • mathematical models
  • security and privacy
  • privacy-enhancing technologies

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Published Papers (2 papers)

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Research

24 pages, 886 KiB  
Article
Double Security Level Protection Based on Chaotic Maps and SVD for Medical Images
by Conghuan Ye, Shenglong Tan, Jun Wang, Li Shi, Qiankun Zuo and Bing Xiong
Mathematics 2025, 13(2), 182; https://doi.org/10.3390/math13020182 - 8 Jan 2025
Viewed by 941
Abstract
The widespread distribution of medical images in smart healthcare systems will cause privacy concerns. The unauthorized sharing of decrypted medical images remains uncontrollable, though image encryption can discourage privacy disclosure. This research proposes a double-level security scheme for medical images to overcome this [...] Read more.
The widespread distribution of medical images in smart healthcare systems will cause privacy concerns. The unauthorized sharing of decrypted medical images remains uncontrollable, though image encryption can discourage privacy disclosure. This research proposes a double-level security scheme for medical images to overcome this problem. The proposed joint encryption and watermarking scheme is based on singular-value decomposition (SVD) and chaotic maps. First, three different random sequences are used to encrypt the LL subband in the discrete wavelet transform (DWT) domain; then, HL and LH sub-bands are embedded with watermark information; in the end, we obtain the watermarked and encrypted image with the inverse DWT (IDWT) transform. In this study, SVD is used for watermarking and encryption in the DWT domain. The main originality is that decryption and watermark extraction can be performed separately. Experimental results demonstrate the superiority of the proposed method in key spaces (10225), PSNR (76.2543), and UACI (0.3329). In this implementation, the following key achievements are attained. First, our scheme can meet requests of different security levels. Second, encryption and watermarking can be performed separately. Third, the watermark can be detected in the encrypted domain. Thus, experiment results and security analysis demonstrate the effectiveness of the proposed scheme. Full article
(This article belongs to the Special Issue Mathematical Models in Information Security and Cryptography)
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14 pages, 891 KiB  
Article
A Lightweight Malware Detection Model Based on Knowledge Distillation
by Chunyu Miao, Liang Kou, Jilin Zhang and Guozhong Dong
Mathematics 2024, 12(24), 4009; https://doi.org/10.3390/math12244009 - 20 Dec 2024
Cited by 1 | Viewed by 1185
Abstract
The extremely destructive nature of malware has become a major threat to Internet security. The research on malware detection techniques has been evolving. Deep learning-based malware detection methods have achieved good results by using large-scale, pre-trained models. However, these models are complex, have [...] Read more.
The extremely destructive nature of malware has become a major threat to Internet security. The research on malware detection techniques has been evolving. Deep learning-based malware detection methods have achieved good results by using large-scale, pre-trained models. However, these models are complex, have large parameters, and require a large amount of hardware resources and have a high inference time cost when applied. To address this challenge, this paper proposes DistillMal, a new method for lightweight malware detection based on knowledge distillation, which improves performance by using a student network to learn valuable cueing knowledge from a teacher network to achieve a lightweight model. We conducted extensive experiments on two new datasets and showed that the student network model’s performance is very close to that of the original model and the outperforms it on some metrics. Our approach helps address the resource constraints and computational challenges faced by traditional deep learning large models. Our research highlights the potential of using knowledge distillation to develop lightweight malware detection models. Full article
(This article belongs to the Special Issue Mathematical Models in Information Security and Cryptography)
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