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Mathematics
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Applied Cryptography and Blockchain Security, 2nd Edition
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Applied Cryptography and Blockchain Security, 2nd Edition

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1830

Special Issue Editor

Dr. Hui Cui

E-Mail Website
Guest Editor
Faculty of IT, Monash University, Clayton Campus, Melbourne, Australia
Interests: cryptography; applied cryptography; cloud security; blockchain security; IoT security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Applied Cryptography is about cryptographic algorithms, protocols, and systems that are used in real-world applications to ensure the confidentiality, integrity, and authenticity of data. It involves the design, analysis, and implementation of cryptographic techniques and protocols that can withstand attacks from malicious adversaries. Blockchain Security is concerned with the security and privacy issues associated with blockchain technology. Blockchain is a distributed ledger technology that is used to store and share data securely and transparently. Blockchain Security involves the design, analysis, and implementation of security measures that protect the data and transactions stored on the blockchain from malicious attacks.

With the increasing trend in Blockchain applications, it is crucial to develop cryptographic techniques that can be applied in blockchain systems to enhance their security and privacy. 

This Special Issue will focus on recent studies of cryptographic solutions in enhancing the security of blockchain applications. Topics include, but are not limited to

Cryptographic primitives and protocols for blockchain applications;

Privacy and anonymity in blockchain systems;

Consensus algorithms and their security analysis;

Smart contract security;

Attacks and vulnerabilities in blockchain systems;

Blockchain-based solutions for secure and private data sharing.

Dr. Hui Cui
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 250 words) can be sent to the Editorial Office for assessment.

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

  • applied cryptography
  • cryptographic algorithms
  • consensus algorithms
  • blockchain security
  • blockchain applications

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Published Papers (2 papers)

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Research

23 pages, 5282 KB  
Article
IoT-SBIdM: A Privacy-Preserving Stateless Blockchain-Based Identity Management for Trustworthy Internet of Things IoT Ecosystems
by Eman Alatawi, Anoud Alhawiti, Doaa Albalawi and Umar Albalawi
Mathematics 2026, 14(4), 715; https://doi.org/10.3390/math14040715 - 18 Feb 2026
Viewed by 694
Abstract
The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices generating and exchanging sensitive data across diverse domains, which introduces challenges in identity management (IdM) regarding privacy, scalability, and verifiability. While blockchain technology provides decentralization and tamper [...] Read more.
The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices generating and exchanging sensitive data across diverse domains, which introduces challenges in identity management (IdM) regarding privacy, scalability, and verifiability. While blockchain technology provides decentralization and tamper resistance, its transparency and increasing on-chain storage demands make it unsuitable for large-scale IoT identity ecosystems. To overcome these challenges, IoT-SBIdM is proposed as a lightweight, privacy-preserving, and stateless blockchain-based identity management framework designed for IoT environments. This framework incorporates Elliptic Curve Cryptography (ECC)-based accumulators and Zero-Knowledge Proofs (ZKPs) to facilitate selective disclosure, enabling entities to prove credential authenticity without exposing sensitive identity information. Furthermore, the framework adopts W3C-compliant Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to promote interoperability and user-controlled identity ownership. The experimental results indicate that IoT-SBIdM achieves efficient smart contract execution by reducing gas costs through optimized registry logic. Moreover, the system maintains a compact block size of only 45 MB at higher block heights, outperforming comparable schemes in storage efficiency by achieving a 55% reduction relative to recent models and an approximate 94% reduction relative to older systems, thereby demonstrating superior scalability and storage efficiency, making it suitable for identity management solutions for IoT environments. Full article
(This article belongs to the Special Issue Applied Cryptography and Blockchain Security, 2nd Edition)
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29 pages, 2296 KB  
Article
V-MHESA: A Verifiable Masking and Homomorphic Encryption-Combined Secure Aggregation Strategy for Privacy-Preserving Federated Learning
by Soyoung Park and Jeonghee Chi
Mathematics 2025, 13(22), 3687; https://doi.org/10.3390/math13223687 - 17 Nov 2025
Cited by 1 | Viewed by 768
Abstract
In federated learning, secure aggregation is essential to protect the confidentiality of local model updates, ensuring that the server can access only the aggregated result without exposing individual contributions. However, conventional secure aggregation schemes lack mechanisms that allow participating nodes to verify whether [...] Read more.
In federated learning, secure aggregation is essential to protect the confidentiality of local model updates, ensuring that the server can access only the aggregated result without exposing individual contributions. However, conventional secure aggregation schemes lack mechanisms that allow participating nodes to verify whether the aggregation has been performed correctly, thereby raising concerns about the integrity of the global model. To address this limitation, we propose V-MHESA (Verifiable Masking-and-Homomorphic Encryption–combined Secure Aggregation), an enhanced protocol extending our previous MHESA scheme. V-MHESA incorporates verification tokens and shared-key management to simultaneously ensure verifiability, confidentiality, and authentication. Each node generates masked updates using its own mask, the server’s secret, and a node-only shared random nonce, ensuring that only the server can compute a blinded global update while the actual global model remains accessible solely to the nodes. Verification tokens corresponding to randomly selected model parameters enable nodes to efficiently verify the correctness of the aggregated model with minimal communication overhead. Moreover, the protocol achieves inherent authentication of the server and legitimate nodes and remains robust under node dropout scenarios. The confidentiality of local updates and the unforgeability of verification tokens are analyzed under the honest-but-curious threat model, and experimental evaluations on the MNIST dataset demonstrate that V-MHESA achieves accuracy comparable to prior MHESA while introducing only negligible computational and communication overhead. Full article
(This article belongs to the Special Issue Applied Cryptography and Blockchain Security, 2nd Edition)
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