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Cryptography, Volume 9, Issue 4 (December 2025) – 6 articles

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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 146

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 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 244

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 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 329

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 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 414

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 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 582

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. 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 382

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.

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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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