Blockchain Papers

Swati Kumari, John Keaney, Hitesh Tewari

No abstract is available for this record.

Francis Kagai, Philip Branch, Jason But, Rebecca Allen

No abstract is available for this record.

Yutian Wang, Dezhi Xia, Mingxi Chen, Yifan Dong · 5 authors

No abstract is available for this record.

Dong-Seong Kim, Ikechi Saviour Igboanusi, George Chidera Akor

No abstract is available for this record.

Tereza Burianová, Martin Perešíni, Ivan Homoliak

No abstract is available for this record.

Yanzhen Li, R. Wang

Execution Tickets (ET) have emerged as a leading proposal for mitigating MEV-related centralization risks by internalizing MEV through a protocol-level lottery system. This paper provides an empirical game-theoretic analysis (EGTA) of the ET mechanism under an infinite-supply design, modeled as a Tullock contest. We evaluate a 2-slot lookahead window as a minimal temporal design that limits multi-slot MEV while preserving support for user pre-confirmations. By introducing a forfeiture parameter, we parameterize a continuum between All-Pay and Winner-Pay regimes. We then map the fairness-revenue frontier, revealing a fundamental design tension: higher contest decisiveness and forfeiture rates can improve protocol revenue, but may reduce allocation fairness by entrenching dominant builders. We identify a quantitative Goldilocks zone that balances MEV-capture efficiency with market diversity.

Sandhya Jatav, Smita Anthanere parte

This research paper analyzes the dynamic and transformative realm of crypto currencies, with a primary focus on their technological foundations, economic implications, and regulatory challenges. Beginning with an examination of the genesis and evolution of prominent crypto currencies, particularly Bit coin, the study delves into the decentralized nature and cryptographic principles that underpin these digital assets. Beyond their role as alternative forms of currency, the research investigates the broader impact of Block chain technology, unraveling its applications across diverse industries. Economic considerations form a pivotal part of the analysis, focusing on financial inclusion, the emergence of decentralized finance platforms, and the innovative concept of non-fungible tokens. The paper scrutinizes the regulatory landscape surrounding crypto currencies, exploring the varied approaches adopted globally and the resulting implications for market participants. In addressing the challenges associated with crypto currencies, including scalability concerns, environmental sustainability, and market volatility, the research offers a nuanced perspective on the intricacies of this evolving ecosystem. Through this comprehensive exploration, the paper contributes valuable insights for academics, policymakers, and industry stakeholders, fostering a deeper understanding of the multifaceted dynamics inherent in the world of crypto currencies. The regulatory landscape for crypto currencies is dynamic and varies globally. Some jurisdictions embrace these digital assets, formulating comprehensive frameworks to balance innovation with investor protection, while others adopt a cautious or restrictive approach due to concerns about volatility and illicit activities. The regulatory challenges include the need for international collaboration and harmonization to address the cross-border nature of crypto currencies.

Azkia (IAI Darussalam Martapura), Annisa Nur Aulia Purnama (IAI Darussalam Martapura), Muhammad Sauqi (IAI Darussalam Martapura)

Penelitian ini bertujuan untuk mengkaji konsep kepemilikan aset digital melalui perspektif m?l (harta) dalam fikih kontemporer. Perkembangan teknologi digital telah melahirkan berbagai bentuk aset baru, seperti cryptocurrency, token digital, non-fungible token (NFT), serta aset berbasis blockchain lainnya yang memiliki nilai ekonomi dan diperdagangkan dalam sistem ekonomi modern. Fenomena ini menimbulkan pertanyaan mengenai status hukum dan kedudukan aset digital dalam perspektif hukum Islam. Penelitian ini menggunakan pendekatan kualitatif dengan metode studi kepustakaan (library research), melalui penelaahan terhadap literatur fikih klasik dan kontemporer, buku ekonomi Islam, serta artikel ilmiah yang relevan dengan perkembangan aset digital. Analisis dilakukan secara deskriptif-analitis untuk mengkaji kesesuaian karakteristik aset digital dengan konsep m?l dalam fikih Islam. Hasil penelitian menunjukkan bahwa aset digital pada dasarnya dapat dikategorikan sebagai m?l, karena memenuhi kriteria utama harta dalam hukum Islam, yaitu memiliki nilai ekonomi, dapat dimiliki secara sah, serta memberikan manfaat bagi pemiliknya. Namun demikian, status hukum beberapa jenis aset digital masih menjadi perdebatan di kalangan ulama, terutama yang memiliki tingkat volatilitas tinggi dan mengandung unsur spekulatif. Dengan demikian, fikih kontemporer memiliki peran penting dalam memberikan landasan ijtihad terhadap fenomena ekonomi digital agar tetap selaras dengan prinsip-prinsip syariah. Penelitian ini diharapkan dapat memberikan kontribusi akademik dalam pengembangan kajian fikih muamalah, khususnya terkait kepemilikan aset digital dalam konteks ekonomi modern.

Nevin Oommen, Atharva Naitam, Ayush Kshirsagar, Atharva Bhede · 5 authors

No abstract is available for this record.

Aggelos Kiayias, Michael Schaller, Orfeas Stefanos Thyfronitis Litos

No abstract is available for this record.

Assignee Research

This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does the robustness of Fed-DPRoC scale with the number of Byzantine clients compared to baseline federated averaging in cross-domain settings such as federated natural language processing tasks. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the robustness of Fed-DPRoC scale with the number of Byzantine clients compared to baseline federated averaging in cross-domain settings such as federated natural language processing tasks (e.g., GLUE benchmark)? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.

Ivan Efimov, Yash Madhwal, Yury Yanovich

No abstract is available for this record.

Xiang LIU, Yao ZHAO, Ping Fan Ke

Major sociopolitical events can reshape public attention toward identity-related issues, potentially influencing valuation patterns in digital markets where identity-related characteristics are embedded in digital assets. Using the overturning of Roe v. Wade as an exogenous policy shock, this paper examines how gender attributes represented in non-fungible token (NFT) avatars affect market outcomes. Using transaction data from six major avatar-based NFT collections traded on Etherscan in 2022, we apply a quasi-experimental design combining propensity score matching and a difference-in-differences model. The results indicate that the policy shock significantly increased the resale prices of NFTs representing female avatars. These findings suggest that sociopolitical shocks can change investor attention and pricing dynamics in decentralized digital markets by highlighting identity-related signals embedded in digital assets.

Samukeliso Mabarani, Mohammad Saidur Rahman, Iqbal Gondal, H. M. N. Dilum Bandara

The tokenization of real-world assets (RWAs) through non-fungible tokens (NFTs) has introduced new opportunities for liquidity, enabling fractional ownership of traditionally illiquid assets. Yet, current NFT fractionalization models remain static, lacking adaptive governance and real-time responsiveness required for managing the dynamic nature of RWAs. This paper presents an Adaptive NFT Fractionalization Framework with Rights Segregation that integrates modular smart contracts, oracle data, and machine learning (ML) insights to enable dynamic rights management. The framework segregates and defines distinct rights, governed through cross-layer decision-making and adaptive rights management that updates allocations based on market data and predictive analytics. Experimental results demonstrate accurate, real-time adjustments of fractional rights, consistent governance execution, and efficient gas utilization across stress and concurrency tests. The findings validate the framework's scalability, responsiveness, and cost-effectiveness, establishing it as a viable approach for adaptive, data-driven management of fractionalized RWAs.

Rahul Kumar Tiwari, Dr. Ram Babu

This article examines human-artificial intelligence (AI) teaming in Ukrainian combat operations from 2022 to 2025, exploring the integration of AI systems with human decision-making in military contexts and how crisis-driven innovation can lead to human-AI teaming. The research addresses three questions: the effectiveness of human-AI teams compared with human-only or fully autonomous systems; the effect of organizational structures on the sustainability of AI integration; and strategic implications for the development of the doctrine and international security governance in the case of the North Atlantic Treaty Organization (NATO). The methodology uses mixed-method comparative case study analysis of three different Ukrainian systems: the Geographic Information System (GIS) Arta geospatial intelligence platform, the reconnaissance-strike unmanned aerial vehicle complex, and volunteer-supported decentralized targeting networks. Data collection was a combination of technical reports, operational battlefield metrics, and the standards of the NATO doctrines. Findings show that Ukrainian human-AI systems show good tactical performance in permissive electromagnetic environments with response times of 30 to 45 seconds and targeting accuracy of two meters but have significant vulnerabilities to electronic warfare—31% mission failure rates. Volunteer networks are highly resilient and have slower decision cycles. The research adds to strategic security, deterrence theory, military innovation theory, and organizational theory through the identification of mechanisms by which human-AI systems affect the stability of deterrence and offers recommendations for the development of the doctrine and international governance of AI for NATO.

Shikah J. Alsunaidi, Hamoud Aljamaan

Ethereum is a widely adopted blockchain platform that supports a large number of decentralized applications. Despite its rapid growth, Ethereum remains vulnerable to security threats, particularly phishing attacks that exploit transactional behavior. This study investigates the effectiveness of tree-based ensemble learning models for detecting phishing transactions on the Ethereum network using an imbalanced transaction dataset. Seven tree-based ensemble classifiers are empirically evaluated under a cost-sensitive learning framework, with performance assessed using the Matthews Correlation Coefficient (MCC) as the primary metric. The results show that boosting-based ensembles substantially outperform bagging-based approaches and a single decision tree. In particular, Gradient Boosting achieves the strongest detection performance with an MCC of 0.9742, while CatBoost provides a trade-off between detection performance and computational efficiency, achieving competitive detection accuracy with the lowest average inference time (approximately 1.54 µs per transaction). The findings demonstrate that accurate and robust phishing detection can be achieved using a compact feature representation, enabling practical deployment with reduced computational overhead.

Assal Aminian, Zining Wang

Cryptocurrency fraud on blockchain platforms continues to cause substantial financial losses, creating an urgent need for detection systems that are not only accurate but also interpretable for operational and regulatory use. In this paper, we propose an explainable framework for Ethereum fraud detection integrating an XGBoost ensemble with TreeSHAP. This system achieves high predictive performance (96.3% F1-score, 96.6% recall) while providing model-level transparency via an interactive chatbot interface. Evaluation using fidelity and stability metrics confirms the reliability of the SHAP-based insights, while user-role simulations demonstrate that our structured delivery enhances clarity and actionability over standard visualizations. This work offers a practical, transparent foundation for deploying robust AI in high-risk financial environments without sacrificing accuracy.

İsmail Alper Sönmezler

NFT (Non-Fungible Token), son yıllarda kripto varlık ekosisteminde önemli bir dönüşüm yaratmış dijital varlıklardır. Dijital sanat eserlerinden koleksiyonluk eşyalara, oyun içi varlıklardan sanal gayrimenkullere kadar birçok alanda kullanılmakta olup, dijital içeriklerin özgünlük ve sahiplik niteliklerini kripto varlık biçiminde temsil etmektedir. Bu çalışmada NFT kavramı, tarihsel gelişimi, kullanım alanları ve türleriyle, NFT’lerin güvenilirliğini sağlayan Blokzincir, ikinci nesli Ethereum ile akıllı sözleşmeler gibi teknik yapısı üzerinde durularak hukuki niteliği konusunda değerlendirmeler yer almaktadır.

Jyoti*, Sakshi, Aashima, Riya

Non-Fungible Tokens (NFTs) are unique digital assets built on blockchain technology that can represent ownership of data or digital items. Although widely associated with digital art and collectibles, NFTs are increasingly being explored for healthcare applications.[1] This review examines how NFTs could be used in managing health data, improving supply chains, enabling secure identities, and supporting emerging digital health services. While NFTs show promise in enhancing transparency, security, and patient control, their adoption is still limited due to technical, regulatory, and ethical challenges.[2]

Tamer Abdelaziz, Karim Ali

No abstract is available for this record.

E. Onofri, Andrea Ciccotelli, Roberto Di Pietro

Oblivious Transfer (OT) is a fundamental cryptographic primitive enabling privacy-preserving computation and constitutes a core building block for secure multi-party computation while supporting a wide range of security-sensitive applications: private information retrieval, zero-knowledge proofs, and password-authenticated key exchange, to cite a few. While recent advances in OT extension have significantly reduced amortised costs, their reliance on batches of random base OTs and substantial pre-computation phases limits their practicality in scenarios where the number of transfers is modest or where communication latency and client-side computation are critical constraints. In such settings, efficient base OT protocols remain both relevant and necessary. In this work, we introduce $I$-$(OT)^2$, a novel base 1-out-of-2 OT protocol grounded in the quadratic residuosity problem, specifically designed to minimise receiver-side computation and interaction. Our construction is particularly appealing on client--server architectures in which the receiver operates on low-power hardware, such as Internet of Things (IoT) devices. Through a lightweight offline pre-computation phase, $I$-$(OT)^2$ shifts the on-transfer computational burden almost entirely to the Sender, while reducing online communication to only six messages and four digests exchanged. We provide a detailed description of the protocol, accompanied by a formal proof of its security. Moreover, to demonstrate the viability of $I$-$(OT)^2$, we also present an open-source proof-of-concept implementation (in C language) evaluated on real IoT hardware. Results are staggering: for 128-bit security using a 3072-bit RSA modulus, the receiver incurs an average online cost per OT as low as 2.80 μs on desktop platforms and 39.90 μs on IoT devices, more than 10$\times$ faster than the well known SimplestOT.

André Schrottenloher

Shor's algorithm represents the main threat of quantum computers to cryptography. In order to precisely understand its feasibility, many authors have worked towards reducing its costs, either at the logical level (assuming a fault-tolerant architecture), or at the physical level (taking into account the constraints of envisioned hardware). In particular, recent works by Chevignard et al. (CRYPTO 2024) and Gidney (arXiv 2025) used improved arithmetic to significantly reduce the qubit cost of factoring RSA public keys. Even more recently, Babbush et al. (arXiv 2026) improved the cost of computing elliptic curve discrete logarithms, with a reduction of a factor 2 to 3 in gate count and qubit count compared to a previous work by Litinski (arXiv 2023). Their result relies on optimized point addition circuits on elliptic curves over prime fields. However they did not reveal their logical quantum circuits, relying instead on a zero-knowledge proof. In this paper, we detail a quantum logical circuit architecture which gives similar results as Babbush et al., with a slightly higher number of qubits (around 1.5% increase) and a slightly smaller Toffoli gate count (between 6.5% and 10% reduction) for the curve secp256k1. We also give gate counts for a generic variant of the circuit, which is valid for any prime field.

Wenpeng Guo, Yujie Wan, Shaojie Yuan, Lin Chang · 6 authors

No abstract is available for this record.

Rumaisha Afrina, Riyanarto Sarno, Kelly Rossa Sungkono, Abdullah Faqih Septiyanyo · 9 authors

No abstract is available for this record.