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Mathematics
Special Issues
Applied Cryptography and Blockchain
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Applied Cryptography and Blockchain

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 3917

Special Issue Editor

Prof. Dr. Miodrag Mihaljevic

E-Mail Website
Guest Editor
Mathematical Institute, The Serbian Academy of Sciences and Arts, 11000 Belgrade, Serbia
Interests: cryptology; information security; blockchain technology; elements of coding theory; machine learning

Special Issue Information

Dear Colleagues,

Recent trends in the combination of cyberspace and physical systems have enhanced the importance of cybersecurity and privacy requirements. In one instance, blockchain technology can be used to solve such challenges. Concurrently, a synergy of cryptography, blockchain, and machine learning has been recognized as important for addressing certain challenges and developing advanced systems. Within this domain, various novel mathematical approaches are required. The goals of this Special Issue include, but are not limited to, proposals of advanced techniques that (i) minimize the implementation overheads of the employed cryptographic mechanisms and/or provide mathematical proofs for the security claims; (ii) address network security and privacy issues; (iii) evaluate the security of certain cryptographic primitives; (iv) yield efficient blockchain consensus protocols; (v) address the synergy of cryptography, blockchain, and machine learning. Accordingly, this Special Issue is focused on a number of topics, including, but not limited to, the following:

  • The design and analysis of advanced lightweight cryptographic techniques;
  • Techniques for network security and privacy;
  • Advanced techniques for blockchain technology;
  • Advanced approaches for blockchain-based federated machine learning;
  • Advanced applications of the above

Prof. Dr. Miodrag Mihaljevic
Guest Editor

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

  • lightweight cryptography
  • network security and privacy
  • blockchain consensus protocols
  • federated machine learning
  • synergy of cryptography, blockchain, and machine learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 803 KiB  
Article
Effective Route Recommendation Leveraging Differentially Private Location Data
by Jongwook Kim
Mathematics 2024, 12(19), 2977; https://doi.org/10.3390/math12192977 - 25 Sep 2024
Viewed by 861
Abstract
The proliferation of GPS-enabled devices and advances in positioning technologies have greatly facilitated the collection of user location data, making them valuable across various domains. One of the most common and practical uses of these location datasets is to recommend the most probable [...] Read more.
The proliferation of GPS-enabled devices and advances in positioning technologies have greatly facilitated the collection of user location data, making them valuable across various domains. One of the most common and practical uses of these location datasets is to recommend the most probable route between two locations to users. Traditional algorithms for route recommendation rely on true trajectory data collected from users, which raises significant privacy concerns due to the personal information often contained in location data. Therefore, in this paper, we propose a novel framework for computing optimal routes using location data collected through differential privacy (DP)-based privacy-preserving methods. The proposed framework introduces a method for accurately extracting transitional probabilities from perturbed trajectory datasets, addressing the challenge of low data utility caused by DP-based methods. Specifically, to effectively compute transitional probabilities, we present a density-adjusted sampling method that enables the collection of representative data across all areas. In addition, we introduce an effective scheme to approximately estimate transitional probabilities based on sampled datasets. Experimental results on real-world data demonstrate the practical applicability and effectiveness of our framework in computing optimal routes while preserving user privacy. Full article
(This article belongs to the Special Issue Applied Cryptography and Blockchain)
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17 pages, 461 KiB  
Article
LMKCDEY Revisited: Speeding Up Blind Rotation with Signed Evaluation Keys
by Yongwoo Lee
Mathematics 2024, 12(18), 2909; https://doi.org/10.3390/math12182909 - 18 Sep 2024
Viewed by 1037
Abstract
Recently, Lee et al. introduced a novel blind rotation technique utilizing ring automorphisms also known as LMKCDEY. Among known prominent blind rotation methods, LMKCDEY stands out because of its minimal key size and efficient runtime for arbitrary secret keys, although Chillotti et al.’s [...] Read more.
Recently, Lee et al. introduced a novel blind rotation technique utilizing ring automorphisms also known as LMKCDEY. Among known prominent blind rotation methods, LMKCDEY stands out because of its minimal key size and efficient runtime for arbitrary secret keys, although Chillotti et al.’s approach, commonly referred to as CGGI, offers faster runtime when using binary or ternary secrets. In this paper, we propose an enhancement to LMKCDEY’s runtime by incorporating auxiliary keys that encrypt the negated values of secret key elements. Our method not only achieves faster execution than LMKCDEY but also maintains a smaller key size compared to the ternary version of CGGI. Moreover, the proposed technique is compatible with LMKCDEY with only minimal adjustments. Experimental results with OpenFHE demonstrate that our approach can improve bootstrapping runtime by 5–28%, depending on the chosen parameters. Full article
(This article belongs to the Special Issue Applied Cryptography and Blockchain)
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25 pages, 5279 KiB  
Article
Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers
by Adel Binbusayyis
Mathematics 2024, 12(11), 1720; https://doi.org/10.3390/math12111720 - 31 May 2024
Cited by 1 | Viewed by 1423
Abstract
In the modern world, the evolution of the internet supports the automation of several tasks, such as communication, education, sports, etc. Conversely, it is prone to several types of attacks that disturb data transfer in the network. Efficient attack detection is needed to [...] Read more.
In the modern world, the evolution of the internet supports the automation of several tasks, such as communication, education, sports, etc. Conversely, it is prone to several types of attacks that disturb data transfer in the network. Efficient attack detection is needed to avoid the consequences of an attack. Traditionally, manual attack detection is limited by human error, less efficiency, and a time-consuming mechanism. To address the problem, a large number of existing methods focus on several techniques for better efficacy in attack detection. However, improvement is needed in significant factors such as accuracy, handling larger data, over-fitting versus fitting, etc. To tackle this issue, the proposed system utilized a Random Grove Blend in Weighted MLP (Multi-Layer Perceptron) Layers to classify network attacks. The MLP is used for its advantages in solving complex non-linear problems, larger datasets, and high accuracy. Conversely, it is limited by computation and requirements for a great deal of labeled training data. To resolve the issue, a random info grove blend and weight weave layer are incorporated into the MLP mechanism. To attain this, the UNSW–NB15 dataset, which comprises nine types of network attack, is utilized to detect attacks. Moreover, the Scapy tool (2.4.3) is utilized to generate a real-time dataset for classifying types of attack. The efficiency of the presented mechanism is calculated with performance metrics. Furthermore, internal and external comparisons are processed in the respective research to reveal the system’s better efficiency. The proposed model utilizing the advantages of Random Grove Blend in Weighted MLP attained an accuracy of 98%. Correspondingly, the presented system is intended to contribute to the research associated with enhancing network security. Full article
(This article belongs to the Special Issue Applied Cryptography and Blockchain)
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