Blockchain Papers

Colin Winter

No abstract is available for this record.

Murray Rudd

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.

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.

Arnita Sur

The cryptocurrency has represented a revolutionary force in the financial market, with a wide variety of available digital assets that can serve different technological and financial needs. Cryptocurrencies vary considerably. It, therefore, goes without saying that this paper should focus on the wide array of cryptocurrencies, grouping them according to their underlying technology, use cases, and functionalities. It refers to the major classification, including Bitcoin, the first digital currency designed primarily as a unit of store and medium of exchange; altcoins, including alternative cryptocurrencies like Ethereum and Ripple, that should introduce new features and functions such as smart contracts and fast processing of transactions; and tokens, which can be issued and managed on existing blockchain platforms and may range from utility in decentralized applications to representing assets. Such categories of analysis are intended to make it possible to distinguish between the roles and technological innovations connected with each type of cryptocurrency. This research adventure offers insight into how the digital currency landscape is emerging and will impact financial systems, investment strategies, and the regulatory approach. This research goes into a comprehensive review of current literature and case studies, engaged with all types of diverse functionalities and applications of cryptocurrencies, providing foundational understanding to stakeholders and policymakers entering this dynamic field. DOI - https://doi.org/10.65525/SVUP.9788199651548.2026.130-141

Sean Hash

No abstract is available for this record.

Xiaopeng Dai, Qianhong Wu, Mingming Wang, Bo Qin · 7 authors

Transaction propagation delay limits the block interval and is one of the main bottlenecks in improving Bitcoin throughput. However, transaction relay in Bitcoin is entirely voluntary, which results in low bandwidth and high transaction propagation delay. Improving relay motivation by introducing incentives can effectively reduce delay, but it still faces challenges such as Sybil attacks during reward allocation, leakage of network layer privacy, and high on-chain/off-chain overhead. Therefore, this paper proposes Txtail, a practical transaction relay incentive scheme for Bitcoin, based on continuously attaching relay evidence representing the relays’ identity and contribution during transaction propagation. We employ a free pricing mechanism based on the game between relays to allocate rewards fairly. We design an order-insensitive relay evidence structure based on aggregate signatures and public key mapping, which reduces off-chain data overhead while alleviating the leakage of relay paths by obfuscating the relay order. We construct a verifiable lottery mechanism based on Merkle tree commitments to reduce the data that needs to be uploaded to the chain. Both theoretical and experimental results show that Txtail reduces the per-hop off-chain overhead and the overall on-chain overhead by 96.6% and 79.8%, respectively, compared with state-of-the-art baselines, while remaining practical for deployment.

Layal Youssef, Juan Páez‐Farrell

No abstract is available for this record.

Mohd. Rahimie Abd. Karim, Saizal Pinjaman, Izaan Jamil, Azmi Abd. Majid · 5 authors

This study examines the weak-form efficiency and international price integration of Malaysia’s regulated Bitcoin market. Daily closing prices for Bitcoin traded in Malaysian ringgit (BTC/MYR), the international Bitcoin price in US dollars (BTC/USD), and the USD/MYR exchange rate are analysed over the 2021–2026 period using secondary market data. The international Bitcoin price is converted into ringgit to provide a currency-consistent benchmark for the local market. Random-walk behaviour is evaluated using the runs test, Ljung–Box test and variance-ratio test. Market integration is examined through unit-root tests, Engle–Granger cointegration analysis and an error-correction model. The daily results provide mixed evidence regarding weak-form efficiency. Although the runs test does not reject randomness in return signs, the Ljung–Box and variance-ratio results indicate dependence at selected horizons. This dependence becomes weaker in the weekly analysis, suggesting that the efficiency assessment is sensitive to data frequency. The local and international Bitcoin prices are cointegrated, with a long-run coefficient close to unity. The error-correction results further show that deviations from the long-run relationship are corrected over time and that international Bitcoin returns significantly influence short-run local price movements. Nevertheless, a small local price premium and residual volatility clustering remain. Overall, Malaysia’s Bitcoin market is closely integrated with the international market but is not perfectly efficient at all horizons. The findings support policies promoting transparent benchmark pricing, market surveillance, adequate liquidity and volatility-risk controls among Malaysian digital asset exchanges.

Murray Rudd

No abstract is available for this record.

Diego R. Llanos, Javier Guzmán Perote, José D. Vicente-Lorente

No abstract is available for this record.

Boon Chuan Lim

No abstract is available for this record.

Robin Schattmann

No abstract is available for this record.

Leo H. Chan

No abstract is available for this record.

Kaoru Aguilera Katayama

This paper presents a Blueprint theoretical-practical method for covert control over a decentralized network like Bitcoin by manipulating official distribution channels and modifying the client software. The attack, termed the "Great Tribulation Attack," transforms legitimate users into functional zombie nodes that validate blocks under hidden rules or preprogrammed transactions without their knowledge. This technique does not rely on the 51% hashing power but on client deception.

suci AMRUL

No abstract is available for this record.

P PAVITRA, GURURAJ MURTGUDDE

With the increased usage of Bitcoin and othercryptocurrencies, there is a need to address issues related tofraud detection in cryptocurrency systems. Such issuesinclude double-spending, money laundering, and accounthacking, among others, that Bitcoin needs to guard against.However, since Bitcoin is decentralised and transactions arenot reversible, the use of central-system approaches cannotbe applied; thus, an alternative approach must be adopted.The presented project offers a viable method of usingmachine learning for Bitcoin fraud detection. The frauddetection method is real-time, using ensemble stacking,which entails combining multiple machine learning modelsto enhance prediction capabilities. Algorithms to be usedinclude Random Forest, Gradient Boosting (XGBoost,LightGBM), Support Vector Machine (SVM), LogisticRegression, and Isolation Forest. In other words, multiplealgorithms will be used to examine Bitcoin transaction data,such as amounts transacted, transaction frequency, andtransaction patterns. Ensemble stacking allows the use of thestrengths of multiple algorithms, while the real-time functionenhances the applicability of the approach. Scalability isanother critical consideration, especially considering thenumber of Bitcoin users. This is why the use of a Flaskapplication server will be necessary for user datasubmissions, visualisation, and sending fraud notifications.Evaluation will be based on accuracy, precision, recall, andF1-score.Conclusion – The proposed solution appears quiteplausible as the fraud detection through machine learning isefficient, while scalability is one of the main features of theapproach.

Jacob Smagula

I argue that Bitcoin is the latest expression of the recurring American conflict over who controls the terms of money and property, and that the political coalition formed around Bitcoin shares the same characteristics as earlier coalitions formed around this struggle. I review three historical cases that each produced a coalition opposing the existing monetary order: the Bank War of the 1830s, the Free Silver movement of the 1890s, and the populist backlash against the Federal Reserve during the 1979 Farm Crisis and the 2008 Great Recession. I find that Bitcoin’s architecture, which allows for self-custody, permissionless access, and an algorithmically fixed supply, is an answer to the old conflict. Four qualities are found across each of the historical coalitions: relative economic insecurity, distrust of institutional management, ordinary Americans confronting the wealthy and powerful, and heterogeneous political affiliations. Using data from the Nakamoto Project's 2025 Bitcoin Adoption and Sentiment Study, I find each of the four qualities present in the contemporary Bitcoin coalition. I conclude that the Bitcoin coalition today resembles the pre-Bryan silver coalition of the 1880s rather than the consolidated movement of 1896, and its outcome may depend in part on whether it produces a unifying political champion.

Pedro Cosme

No abstract is available for this record.

Asmaa Alkholy, Mariam Essam

No abstract is available for this record.

Richard Hanna Beainy, Cesar Kamel

No abstract is available for this record.

Joshua S. Gans, Scott Duke Kominers

No abstract is available for this record.