Blockchain Papers

Murray Rudd

No abstract is available for this record.

Vugar Usi Zade

Hochflexible Rechenzentren aus dem Bitcoin-Mining können zur Integration volatiler Erzeugung beitragen. Die modulare Technik erlaubt schnelles Hoch- und Herunterfahren, wodurch Netzengpässe entschärft und Überschussstrom genutzt werden können. Internationale Anwendungen zeigen den Nutzen, erste Konzepte für Deutschland entstehen.

Kubatbek Rakhimov

No abstract is available for this record.

Regina V. Molchanova

The article examines the role of distributed ledger (blockchain) technologies as a key institutional factor in the transformation of the digital financial services system in the context of the structural restructuring of the financial sector and the strengthening of requirements for transparency, security and efficiency of settlements. The systemic effects of the introduction of blockchain technologies are highlighted, which determine the reduction of transaction costs, acceleration of settlements, increased trust between counterparties and the formation of new models for organizing financial relations. The impact of distributed registries on changes in coordination mechanisms in the financial system, transformation of traditional mediation institutions and redistribution of roles between financial market participants is disclosed. Particular attention is paid to the limitations and risks of implementing distributed registries associated with institutional inertia, shortage of qualified personnel and difficulties in integrating with existing information systems. The author shows the key focus of the factor of increasing the efficiency of digital financial services and the sustainable development of the financial system in blockchain technology, with appropriate institutional, legal and investment support.

K.A. Putkov, Р.Ю. Овсянников

The research addresses the pressing issue of security, immutability, and data synchronization in interbank interaction systems. Traditional centralized architectures, such as SWIFT, demonstrate vulnerabilities, including risks of unauthorized access and high operational costs for reconciliation and data alignment between participants. The aim of the work is to design a protocol concept based on private Distributed Ledger Technology (DLT), which ensures the creation of a unified, cryptographically secured, and synchronized transaction ledger. The article justifies the choice of a private blockchain model with a Practical Byzantine Fault Tolerance (PBFT) consensus algorithm for achieving agreement among trusted node-participants. Key elements of the protocol are smart contracts for automating settlement business logic and a multi-level cryptographic system with mandatory multi-signature. The result is an architectural model that eliminates the risks of data falsification, ensures end-to-end auditability of all operations, and significantly reduces the time and financial costs associated with post-transaction reconciliation. The issues of compliance of the proposed solution with current regulatory requirements in the financial sector are separately considered.

Onkar Singh

No abstract is available for this record.

Krishna Bajpai

No abstract is available for this record.

Zhanna M. Alieva, Tanzil Sh. Chersieva

The article examines the current state and key developmental trends of the payment system of the Russian Federation under the conditions of financial sector digitalization and unprecedented external sanction pressure. The primary focus is placed on analyzing the performance of the National Payment Card System (NPCS) as a guarantor of the country’s economic stability and technological sovereignty. Based on statistical data, the dynamics of implementing “Mir” cards and the Faster Payments System (FPS) are considered, alongside a mathematical evaluation of the economic efficiency of transactional technologies for commercial enterprises. Key barriers in the sphere of cross-border settlements are identified, and perspective ways to overcome them are proposed, including the integration of the digital ruble and supranational distributed ledger technology platforms.

Cathal Doyle

No abstract is available for this record.

Timothy Gould, Sidike Paheding

In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI is a minimal, interpretable framework demonstrating the feasibility of repurposing core machine learning concepts for a novel task structure. To our knowledge, no existing architectures have demonstrated this ability. Traditional approaches lack this capability, leaving data annotation as a time-consuming process that relies heavily on human effort. Using only standard neural network components, we show that SDTI can be achieved through appropriate problem formulation and architectural design. We evaluate the proposed framework on a range of benchmark tasks and demonstrate its effectiveness in reliably identifying the ground truth out of a set of potential target variables. SDTI outperformed baseline architectures by 14% in F1 score on synthetic task identification benchmarks. These proof-of-concept experiments highlight the future potential of SDTI to reduce dependence on manual annotation and to enhance the scalability of autonomous learning systems in real-world applications.

YONGSHENG ZHU, LIHUA LIU, Qiao Yan, BINTAO HE · 6 authors

No abstract is available for this record.

Anish Banerjee, Brent Waters, David J. Wu

No abstract is available for this record.

Junhee LEE, Yixi Cai Lili lei Lei Li, Gweonho Jeong, Jihye Kim · 6 authors

Forward-secure digital signatures protect the integrity of past signatures, even if the current signing key is compromised. Among forward-secure signature schemes, the method introduced by Lee et al. [1], based on zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs), is particularly notable for achieving constant complexity across all metrics without requiring a predefined maximum time period. However, a naive approach to recursive proof composition results in an excessive amount of redundant computation being repeated for each signing process, which our method reduces significantly. In this paper, we advance a zk-SNARK-based forward-secure signature scheme by significantly improving the efficiency of its signing algorithm. By incorporating commit-and-prove SNARKs, we replace the inner verification process with commit verification within the signing circuit. Furthermore, we employ efficient recursive zk-SNARKs with accumulation and folding schemes to improve the setup and update algorithms. Our implementation demonstrates the practicality of our approach: the signing procedure completes in 0.18 seconds, achieving a 75-fold speedup over the previous scheme, setup time is reduced to 0.71 seconds - over 61 times faster, and public parameters are reduced to 25 MB, more than 16 times smaller.

Zhe Li, Chaoping Xing, Yizhou Yao, Chen Yuan

No abstract is available for this record.

Zhengzhong Jin, Mingqi Lu, Bo Peng, Yue Yu

No abstract is available for this record.

Puneet Bakshi, Saurabh Shinde, Sunita Dhavale

AI agents now coordinate cryptographic tasks such as key management, protocol negotiation, zero-knowledge verification, and anomaly response across heterogeneous systems. Despite rapid progress, practical deployments still face gaps in protocol interoperability, verifiable privacy, and post-quantum readiness that hinder trustworthy adoption. This survey systematizes the field across cryptanalysis, cryptographic design, and secure multi-agent coordination, structuring the discussion around protocol families (MCP, A2A, ACP, ANP) and core primitives (MPC, HE, ZKP, PQC). Contributions include a two-dimensional taxonomy, a transparent survey methodology, a state-of-the-art comparison using shared criteria (interpretability, robustness, scalability, PQC readiness, proof overhead), an explicit limitations analysis, and practice-oriented guidance for engineering and governance. The synthesis clarifies trade-offs and provides a deployment roadmap for agentic cryptography in IoT, finance, and identity systems.

Emela Enyinna

No abstract is available for this record.

Xinpeng Huang, Wangjie Qiu, Wanqing Jie, Qing Xia · 9 authors

The rapid growth of decentralized finance (DeFi) has spurred innovation but also exposed blockchain systems to severe security threats. As of November 2025, cumulative losses from blockchain security incidents have exceeded${\$}$36.89 billion. Flash loan attacks account for 135 reported cases and rank fourth among all attack methods. Existing detection approaches either analyze contract source code, which is unavailable for many deployed contracts, or use transaction pattern matching tailored to specific scenarios, and therefore generalize poorly to diverse flash loan attacks. In this paper, we presentFlashShield, a general flash loan attack detection framework based on Hypergraph Neural Networks (HGNNs). We construct comprehensive datasets containing attack and benign transactions across multiple chains, and systematically analyze flash loan attack mechanisms along four DeFi protocol layers: code implementation, business logic, economic mechanisms, and cross protocol interactions.FlashShieldrepresents each transaction as a hypergraph of transfer actions and semantic relations, and employs a hybrid architecture that integrates spectral, spatial, and original features together with both node level and graph level representations. Experiments show thatFlashShieldimproves recall by 29% over leading methods and identifies 43 previously unknown malicious or suspicious activities (18 confirmed flash loan-related exploits and 25 suspected address poisoning incidents), demonstrating its effectiveness and scalability for automated DeFi security monitoring.

Salvatore Furnari

No abstract is available for this record.

David Krause

No abstract is available for this record.

Abhay Kumar R J, Dr. Bhavya Vikas, Dr. Sharath Ambrosse

The financial sector has been revolutionized by blockchain technology and digital assets, offering novel investment opportunities. Stable coins, in particular, have risen to the fore for their blend of blockchain benefits and moderate price fluctuations. Stable coins differ from other cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), which are known for their volatile price swings, with their stable value, meaning they can be employed in payment, trading, decentralized finance (De Fi), and portfolio management. This study aims to assess USDT and DAI's contribution to investment strategies in the current investment landscape between 2022 and 2026 alongside Bitcoin and Ethereum. The secondary data was analysed via time series analysis, 3 year moving average, rolling volatility, market capitalization, and correlation analysis of data obtained from Coin Market Cap, Coin Gecko, Reserve Bank publications and other financial databases. The results show that USDT and DAI possessed less volatility, more price stability and better capital preservation when compared to traditional cryptocurrencies. The study also finds that inflation, interest rates and US Dollar Index (DXY) affect the performance of stable coins and market demand. While there are regulatory, transparency, and market trust issues to address, stable coins have proven to be a potentially low-risk digital asset. In conclusion, according to the study, USDT and DAI are good investment alternatives for those who are looking for stability in the cryptocurrency market and are either conservative or new investors.

Hongtao Tang, Yuxiang Liu, Mingdong Tang

No abstract is available for this record.

Xiushu Jin, Taisei Takahashi, Kazumasa Omote

Protocol-level validity mechanisms, such as validity rollups and STARK-based proving systems, improve blockchain scalability, but do not remove the cost of executing computation-intensive application logic inside smart contracts. At the application layer, a meaningful class of structured arithmetic workloads—including polynomial evaluations, cryptographic primitive computations, and signal-processing operations over finite fields—arises naturally in contract logic, yet remains subject to the on-chain fee model regardless of protocol-level optimizations. This paper investigates whether outsourcing such workloads and replacing direct execution with transparent on-chain verification can provide a practical cost advantage under realistic smart-contract fee models. We present a cross-platform implementation of GKR-based transparent on-chain verification for outsourced structured arithmetic circuits on the Ethereum Virtual Machine (Solidity) and Starknet (Cairo). GKR requires no trusted setup, which makes it particularly suitable for deployment in permissionless public blockchain environments where trust assumptions must be minimized. The design targets layered circuits with regular structure and incorporates implementation techniques that reduce verifier-side overhead and eliminate the linear input-handling bottleneck in naive deployments. These workloads can be represented as layered arithmetic circuits with regular wiring and logarithmic depth. Our evaluation shows that direct execution cost increases rapidly with input size, whereas GKR-based verification scales much more favorably. At size 4096, verification reduces execution cost by approximately 84% on the EVM and 91% on Starknet. These results identify outsourced execution with on-chain GKR verification as a practical design point for application-layer verifiable computation, especially in public blockchain environments where transparency and the absence of trusted setup are important.

Soraya González-Mendes, João Tiago Aparício, Carlos J. Costa

No abstract is available for this record.