Cryptography

28 pages, 415 KB

Open AccessArticle

A Scalable Symmetric Cryptographic Scheme Based on Latin Square, Permutations, and Reed-Muller Codes for Resilient Encryption

by Hussain Ahmad and Carolin Hannusch

Cryptography 2025, 9(4), 70; https://doi.org/10.3390/cryptography9040070 - 31 Oct 2025

Viewed by 108

Abstract

Symmetric cryptography is essential for secure communication as it ensures confidentiality by using shared secret keys. This paper proposes a novel substitution-permutation network (SPN) that integrates Latin squares, permutations, and Reed-Muller (RM) codes to achieve robust security and resilience. As an adaptive design [...] Read more.

Symmetric cryptography is essential for secure communication as it ensures confidentiality by using shared secret keys. This paper proposes a novel substitution-permutation network (SPN) that integrates Latin squares, permutations, and Reed-Muller (RM) codes to achieve robust security and resilience. As an adaptive design using binary representation with base-n Latin square mappings for non-linear substitutions, it supports any n (Codeword length and Latin square order), k (RM code dimension), d (RM code minimum distance) parameters aligned with the Latin square and

RM (n, k, d)

codes. The scheme employs

2 {log}_{2} n

-round transformations using

{log}_{2} n

permutations

ρ_{z}

, where in the additional

{log}_{2} n

rounds, row and column pairs are swapped for each pair of rounds, with key-dependent

π_{z}

permutations for round outputs and fixed

ρ_{z}

permutations for codeword shuffling, ensuring strong diffusion. The scheme leverages dynamic Latin square substitutions for confusion and a vast key space, with permutations ensuring strong diffusion and

RM (n, k, d)

codes correcting transmission errors and enhancing robustness against fault-based attacks. Precomputed components optimize deployment efficiency. The paper presents mathematical foundations, security primitives, and experimental results, including avalanche effect analysis, demonstrating flexibility and balancing enhanced security with computational and storage overhead. Full article

23 pages, 1008 KB

Open AccessArticle

A Lightweight Decentralized Medical Data Sharing Scheme with Dual Verification

by Shaobo Zhang, Yijie Yin, Nangui Chen and Honghui Ning

Cryptography 2025, 9(4), 69; https://doi.org/10.3390/cryptography9040069 - 30 Oct 2025

Viewed by 110

Abstract

The rapid growth of smart healthcare improves medical efficiency through electronic data sharing but introduces security risks like privacy leaks and data tampering. However, existing ciphertext-policy attribute-based encryption faces challenges such as single points of failure, weak authentication, and inadequate integrity protection, hindering [...] Read more.

The rapid growth of smart healthcare improves medical efficiency through electronic data sharing but introduces security risks like privacy leaks and data tampering. However, existing ciphertext-policy attribute-based encryption faces challenges such as single points of failure, weak authentication, and inadequate integrity protection, hindering secure, efficient medical data sharing. Therefore, we propose LDDV, a lightweight decentralized medical data sharing scheme with dual verification. LDDV constructs a lightweight multi-authority collaborative key management architecture based on elliptic curve cryptography, which eliminates the risk of single point of failure and balances reliability and efficiency. Meanwhile, a lightweight dual verification mechanism based on elliptic curve digital signature provides identity authentication and data integrity verification. Security analysis and experimental results show that LDDV achieves 28–42% faster decryption speeds compared to existing schemes and resists specific threats such as chosen plaintext attacks. Full article

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33 pages, 4531 KB

Open AccessArticle

Enhancing Multi-Factor Authentication with Templateless 2D/3D Biometrics and PUF Integration for Securing Smart Devices

by Saloni Jain, Amisha Bagri, Maxime Cambou, Dina Ghanai Miandoab and Bertrand Cambou

Cryptography 2025, 9(4), 68; https://doi.org/10.3390/cryptography9040068 - 27 Oct 2025

Viewed by 252

Abstract

Secure authentication in smart device ecosystems remains a critical challenge, particularly due to the irrevocability of compromised biometric templates in server-based systems. This paper presents a post-quantum secure multi-factor authentication protocol that combines templateless 2D and 3D facial biometrics, liveness detection, and Physical [...] Read more.

Secure authentication in smart device ecosystems remains a critical challenge, particularly due to the irrevocability of compromised biometric templates in server-based systems. This paper presents a post-quantum secure multi-factor authentication protocol that combines templateless 2D and 3D facial biometrics, liveness detection, and Physical Unclonable Functions (PUFs) to achieve robust identity assurance. The protocol exhibits zero-knowledge properties, preventing adversaries from identifying whether authentication failure is due to the biometric, password, PUF, or liveness factor. The proposed protocol utilizes advanced facial landmark detection via dlib or mediapipe, capturing multi-angle facial data and mapping it. By applying a double-masking technique and measuring distances between randomized points, stabilized facial landmarks are selected through multiple images captured during enrollment to ensure template stability. The protocol creates high-entropy cryptographic keys, securely erasing all raw biometric data and sensitive keys immediately after processing. All key cryptographic operations and challenge-response exchanges employ post-quantum algorithms, providing resistance to both classical and quantum adversaries. To further enhance reliability, advanced error-correction methods mitigate noise in biometric and PUF responses, resulting in minimal FAR and FRR that meets industrial standards and resilience against spoofing. Our experimental results demonstrate this protocol’s suitability for smart devices and IoT deployments requiring high-assurance, scalable, and quantum-resistant authentication. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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23 pages, 13031 KB

Open AccessArticle

Constructing 8 × 8 S-Boxes with Optimal Boolean Function Nonlinearity

by Phuc-Phan Duong and Cong-Kha Pham

Cryptography 2025, 9(4), 67; https://doi.org/10.3390/cryptography9040067 - 21 Oct 2025

Viewed by 360

Abstract

Substitution boxes (S-Boxes) are the core components of modern block ciphers, responsible for introducing the essential nonlinearity that protects against attacks like linear and differential cryptanalysis. For an 8-bit S-Box, the highest possible nonlinearity for a balanced Boolean function is 116. The best [...] Read more.

Substitution boxes (S-Boxes) are the core components of modern block ciphers, responsible for introducing the essential nonlinearity that protects against attacks like linear and differential cryptanalysis. For an 8-bit S-Box, the highest possible nonlinearity for a balanced Boolean function is 116. The best results previously reported in the literature achieved an average nonlinearity of 114.5 across the coordinate Boolean functions of 8 × 8 S-boxes. Our proposed method surpasses this record, producing S-boxes whose coordinate functions exhibit an average nonlinearity of 116. This is a significant achievement as it reaches the best result to date for the nonlinearity of the coordinate Boolean functions of an S-Box. Our S-Box generation method is based on multiplication over the field

G F (2^{4})

and

4 \times 4

component S-Boxes. The approach is also highly effective, capable of producing a large number of S-Boxes with good cryptographic properties. Other cryptographic criteria, such as BIC, SAC, DAP, and LAP, though not fully optimal, remain within acceptable ranges when compared with other reported designs. In addition, a side-channel attack evaluation is presented, covering both parameter analysis and experimental results on a real system when applying the proposed S-Box in the AES algorithm. These results make it a leading solution for block cipher design. Full article

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28 pages, 444 KB

Open AccessArticle

On the Homomorphic Properties of Kyber and McEliece with Application to Post-Quantum Private Set Intersection

by Anas A. Abudaqa, Khaled Alshehri and Muhamad Felemban

Cryptography 2025, 9(4), 66; https://doi.org/10.3390/cryptography9040066 - 20 Oct 2025

Viewed by 401

Abstract

Crystals-Kyber and Classic-McEliece are two prominent post-quantum key encapsulation mechanisms (KEMs) designed to address the challenges posed by quantum computing to classical cryptographic schemes. While the former has been standardized by the National Institute of Standards and Technology (NIST), the latter is well-known [...] Read more.

Crystals-Kyber and Classic-McEliece are two prominent post-quantum key encapsulation mechanisms (KEMs) designed to address the challenges posed by quantum computing to classical cryptographic schemes. While the former has been standardized by the National Institute of Standards and Technology (NIST), the latter is well-known for its exceptional robustness and as one of the finalists of the fourth round of post-quantum cryptography standardization. Private set intersection (PSI) is a privacy-preserving technique that enables two parties, each possessing a dataset, to compute the intersection of their sets without revealing anything else. This can be achieved thanks to homomorphic encryption (HE), which allows computations on encrypted data. In this paper, firstly, we study Kyber and McEliece, apart from being KEMs, as post-quantum public key encryption (PKE), and examine their homomorphic properties. Secondly, we design two different two-party PSI protocols that utilize the homomorphic capabilities of Kyber and McEliece. Thirdly, a practical performance evaluation under NIST’s security levels 1, 3, and 5 is conducted, focusing on three key metrics: storage overhead, communication overhead, and computation cost. Insights indicate that the Kyber-based PSI Protocol, which utilizes the multiplicative homomorphic property, is secure but less efficient. In contrast, the McEliece-based PSI protocol, while efficient in practice, raises concerns regarding its security as a homomorphic encryption scheme. Full article

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18 pages, 1647 KB

Open AccessArticle

A Two-Layer Transaction Network-Based Method for Virtual Currency Address Identity Recognition

by Lingling Xia, Tao Zhu, Zhengjun Jing, Qun Wang, Zhuo Ma, Zimo Huang and Ziyu Yin

Cryptography 2025, 9(4), 65; https://doi.org/10.3390/cryptography9040065 - 11 Oct 2025

Viewed by 608

Abstract

Digital currencies, led by Bitcoin and USDT, are characterized by decentralization and anonymity, which obscure the identities of traders and create a conducive environment for illicit activities such as drug trafficking, money laundering, cyber fraud, and terrorism financing. Focusing on the USDT-TRC20 token [...] Read more.

Digital currencies, led by Bitcoin and USDT, are characterized by decentralization and anonymity, which obscure the identities of traders and create a conducive environment for illicit activities such as drug trafficking, money laundering, cyber fraud, and terrorism financing. Focusing on the USDT-TRC20 token on the Tron blockchain, we propose a two-layer transaction network-based approach for virtual currency address identity recognition for digging out hidden relationships and encrypted assets. Specifically, a two-layer transaction network is constructed: Layer A describes the flow of USDT-TRC20 between on-chain addresses over time, while Layer B represents the flow of TRX between on-chain addresses over time. Subsequently, an identity metric is proposed to determine whether a pair of addresses belongs to the same user or group. Furthermore, transaction records are systematically acquired through blockchain explorers, and the efficacy of the proposed recognition method is empirically validated using dataset from the Key Laboratory of Digital Forensics. Finally, the transaction topology is visualized using Neo4j, providing a comprehensive and intuitive representation of the traced transaction pathways. Full article

(This article belongs to the Section Blockchain Security)

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20 pages, 8727 KB

Open AccessArticle

Comparative Deep Learning-Based Side-Channel Analysis of an FPGA-Based CRYSTALS-Kyber NTT Accelerator

by Munkhbaatar Chinbat, Liji Wu, Xiangmin Zhang, Yifan Yang and Man Wei

Cryptography 2025, 9(4), 64; https://doi.org/10.3390/cryptography9040064 - 9 Oct 2025

Viewed by 705

Abstract

Deep learning-based side-channel analysis is one of the most effective techniques for extracting and classifying sensitive information from a target device. This paper demonstrates the best-performing deep learning model for the target implementation by evaluating various deep learning architectures, including MLP, CNN, and [...] Read more.

Deep learning-based side-channel analysis is one of the most effective techniques for extracting and classifying sensitive information from a target device. This paper demonstrates the best-performing deep learning model for the target implementation by evaluating various deep learning architectures, including MLP, CNN, and RNN, while systematically optimizing their hyperparameters to achieve the best performance. The paper uses a case study of the Number Theoretic Transform accelerator for the CRYSTALS-Kyber key encapsulation mechanism to show that enhanced deep learning analysis can be used to break security. The best-performing deep learning-based model achieved a 96.64% accuracy in classifying pairwise coefficients of the s vector, which is used to generate the secret key with the NTT accelerator for Kyber768 and Kyber1024. For Kyber512, the model achieved an accuracy of 95.71%. The proposed approach significantly improves average training efficiency, with POIs achieving up to 1.45 times faster performance for MLP models, 10.53 times faster for CNNs, and 10.28 times faster for RNNs compared to deep learning methods without POIs, while maintaining high accuracy in side-channel analysis. Full article

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31 pages, 2417 KB

Open AccessArticle

An Optimized Framework for Detecting Suspicious Accounts in the Ethereum Blockchain Network

by Noha E. El-Attar, Marwa H. Salama, Mohamed Abdelfattah and Sanaa Taha

Cryptography 2025, 9(4), 63; https://doi.org/10.3390/cryptography9040063 - 28 Sep 2025

Viewed by 579

Abstract

Detecting, tracking, and preventing cryptocurrency money laundering within blockchain systems is a major challenge for governments worldwide. This paper presents an anomaly detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model [...] Read more.

Detecting, tracking, and preventing cryptocurrency money laundering within blockchain systems is a major challenge for governments worldwide. This paper presents an anomaly detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model employs Particle Swarm Optimization (PSO) to select optimal feature subsets. Additionally, three machine learning algorithms—XGBoost, Isolation Forest (IF), and Support Vector Machine (SVM)—are employed to detect suspicious accounts. A Genetic Algorithm (GA) is further applied to determine the optimal hyperparameters for each machine learning model. The evaluations demonstrate the superiority of the XGBoost algorithm over SVM and IF, particularly when enhanced with GA. It achieved accuracy, precision, recall, and F1-score values of 0.98, 0.97, 0.98, and 0.97, respectively. After applying GA, XGBoost’s performance metrics improved to 0.99 across all categories. Full article

(This article belongs to the Section Blockchain Security)

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39 pages, 505 KB

Open AccessReview

A Survey of Post-Quantum Oblivious Protocols

by Altana Khutsaeva, Anton Leevik and Sergey Bezzateev

Cryptography 2025, 9(4), 62; https://doi.org/10.3390/cryptography9040062 - 27 Sep 2025

Viewed by 1041

Abstract

Modern distributed computing systems and applications with strict privacy requirements demand robust data confidentiality. A primary challenge involves enabling parties to exchange data or perform joint computations. These interactions must avoid revealing private information about the data. Protocols with the obliviousness property, known [...] Read more.

Modern distributed computing systems and applications with strict privacy requirements demand robust data confidentiality. A primary challenge involves enabling parties to exchange data or perform joint computations. These interactions must avoid revealing private information about the data. Protocols with the obliviousness property, known as oblivious protocols, address this issue. They ensure that no party learns more than necessary. This survey analyzes the security and performance of post-quantum oblivious protocols, with a focus on oblivious transfer and oblivious pseudorandom functions. The evaluation assesses resilience against malicious adversaries in the Universal Composability framework. Efficiency is quantified through communication and computational overhead. It identifies optimal scenarios for these protocols. This paper also surveys related primitives, such as oblivious signatures and data structures, along with their applications. Key findings highlight the inherent trade-offs between computational cost and communication complexity in post-quantum oblivious constructions. Open challenges and future research directions are outlined. Emphasis is placed on quantum-resistant designs and formal security proofs in stronger adversarial models. Full article

(This article belongs to the Collection Survey of Cryptographic Topics)

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19 pages, 255 KB

Open AccessReview

From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics

by Hanwei Qian, Lingling Xia, Ruihao Ge, Yiming Fan, Qun Wang and Zhengjun Jing

Cryptography 2025, 9(4), 61; https://doi.org/10.3390/cryptography9040061 - 26 Sep 2025

Viewed by 1077

Abstract

As deepfake technology matures, its risks in spreading false information and threatening personal and societal security are escalating. Despite significant accuracy improvements in existing detection models, their inherent opacity limits their practical application in high-risk areas such as forensic investigations and news verification. [...] Read more.

As deepfake technology matures, its risks in spreading false information and threatening personal and societal security are escalating. Despite significant accuracy improvements in existing detection models, their inherent opacity limits their practical application in high-risk areas such as forensic investigations and news verification. To address this gap in trust, explainability has become a key research focus. This paper provides a systematic review of explainable deepfake detection methods, categorizing them into three main approaches: forensic analysis, which identifies physical or algorithmic manipulation traces; model-centric methods, which enhance transparency through post hoc explanations or pre-designed processes; and multimodal and natural language explanations, which translate results into human-understandable reports. The paper also examines evaluation frameworks, datasets, and current challenges, underscoring the necessity for trustworthy, reliable, and interpretable detection technologies in combating digital misinformation. Full article

42 pages, 2989 KB

Open AccessArticle

Privacy-Driven Classification of Contact Tracing Platforms: Architecture and Adoption Insights

by Sidra Anwar and Jonathan Anderson

Cryptography 2025, 9(4), 60; https://doi.org/10.3390/cryptography9040060 - 24 Sep 2025

Viewed by 701

Abstract

Digital contact-tracing (CT) systems differ in how they process risk and expose data, and the centralized–decentralized dichotomy obscures these choices. We propose a modular six-model classification and evaluate 18 platforms across 12 countries (July 2020–April 2021) using a 24-indicator rubric spanning privacy, security, [...] Read more.

Digital contact-tracing (CT) systems differ in how they process risk and expose data, and the centralized–decentralized dichotomy obscures these choices. We propose a modular six-model classification and evaluate 18 platforms across 12 countries (July 2020–April 2021) using a 24-indicator rubric spanning privacy, security, functionality, and governance. Methods include double-coding with Cohen’s

κ

for inter-rater agreement and a 1000-draw weight-sensitivity check; assumptions and adversaries are stated in a concise threat model. Results: No single model dominates; Bulletin Board and Custodian consistently form the top tier on privacy goals, while Fully Centralized eases verification/notification workflows. Timelines show rapid GAEN uptake and near-contemporaneous open-source releases, with one late outlier. Contributions: (i) A practical, generalizable classification that makes compute-locus and data addressability explicit; (ii) a transparent indicator rubric with an evidence index enabling traceable scoring; and (iii) empirically grounded guidance aligning deployments with goals G1–G3 (PII secrecy, notification authenticity, unlinkability). Limitations include reliance on public documentation and architecture-level (not mechanized) verification; future work targets formal proofs and expanded double-coding. The framework and findings generalize beyond COVID-19 to privacy-preserving digital-health workflows. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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Cryptography, Volume 9, Issue 4 (December 2025) – 11 articles

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