Blockchain Papers

Murray Rudd

No abstract is available for this record.

Kubatbek Rakhimov

No abstract is available for this record.

Onkar Singh

No abstract is available for this record.

Krishna Bajpai

No abstract is available for this record.

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.

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.

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.

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.

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.

Zezhou Xu, Fenglin Wu

No abstract is available for this record.

Jerusa Alberton, Marcelo Cabús Klötzle, Marcelo Guedes Pecly, Carlos de Lamare Bastian-Pinto

No abstract is available for this record.

Andreu Pere Isern-Deyà, M. Francisca Hinarejos, Josep Lluís Ferrer Gomila

Online transactions are becoming increasingly popular, and the purchase and delivery of digital assets is a prominent example. In these transactions, buyers are hesitant to pay for an asset until they receive it, whereas sellers are reluctant to send the asset until they are paid. Unfortunately, actual solutions do not always meet all the requirements to conduct a secure exchange, with fairness being one of the requirements that needs more attention. Historically, solutions to this problem have relied on trusted third parties (TTPs) serving as trusted intermediaries among participants, but the advent of blockchain has enabled the reduction or elimination of TTP involvement in many cases. In this paper, we present a fair blockchain-based solution that does not require any TTP for the secure delivery of digital assets, proving its technical feasibility and cost-effectiveness through assessments on blockchains based on the Ethereum Virtual Machine.

Jakub Kodajek

Bitcoin is considered an anonymous transaction technology. Transactions are not directly linked to real names or physical identities of users. However, each transaction is recorded in the blockchain, which is publicly available and allows anyone to perform detailed analysis. This bachelor thesis deals with the issue of attributing cryptocurrency wallets to specific nodes in the Bitcoin peer-to-peer network. The aim of the thesis is to examine the process of transaction propagation between nodes, identify factors influencing their order and propagation speed, and propose methods that will allow estimating the original node responsible for creating or first sending the transaction. The theoretical part describes the basic mechanisms of transaction propagation in the network and analyzes anonymization and deanonymization techniques. The practical part focuses on the design and implementation of heuristics combining propagation time profiles with topological information about the network. For this purpose, a modular platform was developed in the .NET environment, which enables the analysis of data from the P2P network. The contribution of this work is the combination of theoretical principles of transaction propagation with the practical use of data from a real network and the extension of existing methods for analyzing anonymity in the Bitcoin cryptocurrency environment.

Ammar Ahmed Othman, Seddiq Hassan Al-Banna Ali, Mohammed Bakr Youssef

In the digital currency, Bitcoin (BTC) is called the gold of the digital currency. It is possible to make some profits in trading of bitcoins, though this market is a very illiquid market and it is very challenging to determine the price of a bitcoin. The current work uses historical data and technical indicators to predict Bitcoin prices in a broad approach. BTC-USD price data were obtained using Yahoo Finance API and covered from 01/01/2015 till 07/01/2024. The concept of feature engineering was applied to improve the dataset by including vital financial characteristics, including Moving Averages, RSI, and Bollinger Bands for higher forecasting precision. The forward-looking model for the Bitcoin price was developed using machine learning and deep learning algorithms. The efficiency of the model was assessed with the help of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The overall values of Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error were 0.0062, 8.39e-05 and 0.0092 respectively which suggest that the proposed model is accurate in forecasting the future prices.

Grigorios Rapos

No abstract is available for this record.

Yackolley Amoussou-Guenou, Maarten R.C. van Oordt

No abstract is available for this record.

Steven Paul Nohr

<b><i>State and event validation</i></b> are fundamental for ensuring the correctness and integrity of system states as they transition across decentralized networks. In decentralized systems, such as blockchain or distributed ledgers, maintaining state consistency, triggering actions based on events, and validating those actions across nodes require robust consensus protocols. This paper explores the architecture of state and event validation mechanisms, addressing challenges such as node synchronization, consensus-based event ordering, and error handling in invalid state transitions. By examining the role of validation in maintaining trust and reliability, we highlight its importance in secure and scalable decentralized applications, including smart contracts, financial transactions, and IoT systems.

Hayat Ullah Abid, Syeda Maria Zafar, Muhammad Arslan, Muhammad Essa

Land ownership is a crucial element of society, providing stability, economic opportuni ties, and social identity. However, managing land ownership in Pakistan is complex and challeng ing, plagued by disputes, fraud, and inefficiencies in land markets. Tokenization, derived from Web 3.0 and blockchain technology, offers a promising solution by digitizing land parcels into tokens stored on a secure and transparent distributed ledger. This paper explores how tokenization can enhance the efficiency, transparency, and accessibility of land markets, streamline the verification and transfer of ownership, reduce fraud risks, and improve market liquidity. The study also outlines implementation steps and data requirements for tokenization in Pakistan’s land information sys tem.

Kubatbek Rakhimov

No abstract is available for this record.