Blockchain Papers

Frido Rolloos, Frederic Delaroche, Glenden C Khew, Ksenia Ponomareva

No abstract is available for this record.

Natan Katz, Natan Katz

Smart contracts are a major tool in Ethereum transactions. Therefore, hackers can exploit them by generating malicious code and using their vulnerabilities to perform malicious transactions. This paper presents two successful approaches for detecting malicious contracts: one uses opcode and relies on GPT2, and the other uses the Solidity source and a LORA finetuned CodeLlama. Finally, we present an XGBOOST model that combines gas properties and Hexadecimal signatures for detecting malicious transactions. This approach relies on early assumptions that maliciousness is manifested by the uncommon usage of the contracts’ functions and the effort to pursue the transaction.

Chen Ben Tolila, Yarden Hovav, Kiril Danilchenko, Hadassa Daltrophe

ABSTRACT The rising expenses associated with car ownership have driven individuals to seek more affordable alternatives, such as car rentals. However, conventional car rental services often come with high costs due to leasing companies' overhead expenses. Consequently, car sharing has emerged as a popular and cost‐effective solution that reduces expenses and promotes eco‐friendliness by reducing the number of vehicles on the roads. Nonetheless, centralization and reliability remain persistent challenges in car‐sharing implementation. To address these issues, we propose a decentralized crowd car sharing and renting platform called CROWDCARLINK, leveraging blockchain technology's power. This innovative platform enables individuals and leasing companies to rent vehicles while securely recording each car's maintenance and lease history on the blockchain. Within CROWDCARLINK, garages are pivotal contributors, adding vehicle information in a reliable and immutable manner. By utilizing blockchain technology, our platform ensures transparency and fosters trust, effectively overcoming the limitations imposed by centralization. Our architectural design incorporates smart contracts, which help streamline processes and facilitate seamless transactions within the platform. To demonstrate the feasibility of our approach, we have developed a prototype utilizing a private Ethereum blockchain with Proof of Authority (PoA) consensus. We believe that the architectural design and the practical solution presented here will play an integral role in shaping the future of smart transportation. Our platform aims to benefit individuals and the environment by offering a cost‐effective and efficient solution, paving the way for a more sustainable and advanced transportation ecosystem.

David Krause

No abstract is available for this record.

Asma Alzahrani, Dimah Alahmadi, Nesreen M. Alharbi, Hana Almagrabi

Machine learning has evolved from a lab curiosity to a widely used technology that is fundamentally reliant on ground truth data for model training and evaluation. This research addresses the challenges in obtaining accurate ground truth data due to limited domain expertise, sparse and unrepresentative datasets, and the high costs associated with data acquisition. The quality of this data significantly influences the reliability of machine learning models, prompting research into methods to improve ground-truth reliability. This research proposes a framework that utilize blockchain-based crowdsourcing for ground-truth data annotation. Blockchain technology, with its decentralized immutable ledger system, offers a secure method for data verification and collection from decentralized entities. The proposed framework was implemented in an Ethereum network environment using blockchain technology and smart contracts. Next, we evaluated the collected ground truth by measuring the inter-rater agreement among the participants. The experimental results indicate that blockchain can enhance annotation consistency, showing a higher reliability of crowd-sourced data compared to expert opinions. Most annotator pairs demonstrated moderate to strong agreement, confirming the potential of blockchain technology in improving ground truth data annotation.

Istiaque Ahmed, Kentaroh Toyoda, Tadashi Nakano, Shoji Kasahara · 6 authors

The rapid evolution of digital identity verification demands solutions that balance security, privacy, and efficiency. The electronic know your customer (eKYC) is a technological integration for client identification. It automates the process, reducing costs related to traditional know your customer (KYC). This includes eliminating paper-based document management, reducing manpower needs, and minimizing human errors. This systematic literature review (SLR) uses the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model to investigate the revolutionary potential of blockchain-based electronic KYC (eKYC), focusing on self-sovereign identity (SSI) and Decentralized Identifiers (DID). The evaluation summarizes the current state by critically assessing 44 selected research works from an initial pool of 367. Our findings show that decentralized eKYC improves security with tamper-proof credentials and cryptographic verification. SSI and DID give users control over their data and selective disclosure. However, there are key limitations: 1) a focus on financial applications, ignoring Internet of Things (IoT) integration; 2) a lack of comprehensive technical analysis on scalability and interoperability; and 3) limited real-world case studies on regulatory compliance and challenges. This work combines insights from research and industry, highlighting the need for regulatory collaboration, hybrid architectures for scalability, and user-centric design. In addition, most identity management solutions are based on Ethereum (33%), followed by Hyperledger (18%). Around 51% of solutions use smart contracts, with banking (23%) and the financial industries (19%) being the primary adopters. It emphasizes the importance of standardized eKYC protocols, technical evaluations, and interdisciplinary collaboration for practical adoption across sectors.

Di Perna, Vincenzo Paolo, Foderaro, Michele, Fabris, Francesco, Bernardo, Marco

Blockchain technology is set to transform economics and finance by enabling secure, transparent, and decentralized transactions. Some significant examples in this sense are cryptocurrencies and decentralized finance, which leverage blockchain technology to provide fast, low-cost financial services without a central authority, as well as the tokenization of finance, already forecast by Larry Fink, CEO of BlackRock. As crypto economies and blockchain applications gain global relevance, the need to measure and assess their efficiency is becoming increasingly important. While blockchain efficiency is often evaluated in terms of transactions per second or energy consumption, cryptocurrency efficiency is implicitly assessed through various indexes, such as capitalization, price trends, average transaction value, mining profitability, and others. What is lacking is an index capable of comprehensively and coherently describing the actual functioning of a crypto economic system, accounting for its key economic characteristics – such as supplymechanisms and token distribution – and the level of user participation within the specific crypto economy. In this study, we introduce a new theoretical framework based on Shannon entropy to assess the economic efficiency of a cryptocurrency through the Entropy Balance index (EB-index). Our approach integrates on-chain parameters – sourced from Coin Metrics® – by mapping them to economic quality attributes. To illustrate how our entropy-based approach works, we apply it to two distinct sets of attributes across six leading cryptocurrencies by market capitalization and use-case diversity: Bitcoin, Ethereum, Ripple, USD Coin, Dogecoin, and Cardano. For either set of attributes, the six EB-index values provide us with a comprehensive way of comparing the considered cryptocurrencies from an economic efficiency viewpoint. Our approach is fully customizable with respect to the selection of attributes as well as their weights.

Ferariu, Tudor, Wadler, Philip, Melkonian, Orestis

Good news for researchers in formal verification: smart contracts regularly suffer exploits such as the DAO bug, which lost the equivalent of 60 million USD on Ethereum. This makes a strong case for applying formal methods to guarantee essential properties.<br/><br/>Which properties would we like to prove? Most previous studies focus on contract-specific properties that do not generalize to a wide class of smart contracts. There is currently no commonly agreed upon list of properties to use as a starting point in writing a formal specification.<br/><br/>We propose three properties that we believe are relevant to all smart contracts: Validity, Liquidity, and Fidelity. Focusing on the concrete case of the Cardano platform, we show how these properties stop exploits similar to the DAO bug, as well as preventing other common issues such as the locking of funds and double satisfaction.<br/><br/>We model an account simulation, a multi-signature wallet, and an order book decentralized exchange, as example smart contract specifications using state transition systems in the Agda proof assistant. We formalize the above properties and prove they hold for the models. The models are then separately proven to be functionally equivalent to a validator implementation in Agda, which is translated to Haskell using agda2hs. The Haskell code can then be compiled and put on the Cardano blockchain directly. We use the Cardano Node Emulator to run property-based tests and confirm that our validator works correctly.

Joel Curado, Manila Bhandari, João C. Ferreira, Ana Martins

The maritime supply chain plays a vital role in global trade, but it continues to face major challenges, including transparency issues, fraud, and data privacy concerns. Blockchain technology has emerged as a promising solution to make the supply chain more secure, efficient, and trustworthy across the system. However, it still encounters limitations, especially regarding privacy and the handling of large volumes of data. To address these issues, Zero-Knowledge Proofs (ZKPs) offer a viable solution, enabling the validation of documents and transactions without revealing sensitive information. This helps maintain confidentiality while meeting regulatory requirements, such as those set by the eFTI regulation. This paper investigates blockchain adoption in maritime supply chains with a focus on ZKP integration for secure document verification, fraud mitigation, and regulatory compliance. It evaluates computational overhead, scalability, and adoption barriers, and proposes a framework supported by simulation-based validation using Ethereum and ZoKrates to assess feasibility and performance. By combining ZKPs with blockchain, this approach enhances a secure, transparent, and efficient trade ecosystem, optimising resources and reducing risks. Future research directions are outlined to advance sustainable maritime logistics.

Muhammadamin Dilmurodov

This capstone project explores the integration of decentralized finance (DeFi) functionalities—Token Swapping, Staking/Yield Tracking, and Liquidity Provision—in blockchain explorers within the Ethereum ecosystem. Through a qualitative analysis of 12 explorers, it assesses their support for these functions, revealing a divide between general-purpose platforms focused on data transparency and DeFi-focused platforms offering advanced financial tools. The study highlights varying levels of functionality availability and usability, providing insights into enhancing accessibility for DeFi users.

Ulrich Bindseil, Omid Malekan

The design of financial instruments and processes is contingent on the infrastructure supporting them. Blockchain technology, as utilised by public crypto networks such as Ethereum, represents a novel type of payment and settlement infrastructure that gives rise to a new generation of solutions. This paper discusses the key properties of this technology, including immediacy, omni-asset capability, programmability and its ability to flatten the financial architecture (disintermediation). We illustrate the distinctive products these features make possible, and have the potential to disrupt current payment and capital market systems. We discuss the utility of blockchain technology in private (permissioned) networks. Finally, we revisit the risks of public crypto networks and their mitigants. This article is also included in the Business &amp; Management Collection which can be accessed at https://hstalks.com/business/.

Sang Rae Kim

No abstract is available for this record.

Lukman Adewale Ajao, Buhari Ugbede Umar, Henry Ohiani Ohize, Eustace M. Dogo · 6 authors

The prevalence of political interference during election processes remains a significant challenge to the free, fair, and credible conduct of general elections. This election event is crucial as a pillar of democratic governance in any democracy, with the potential for unfitting disturbance and chaos, such as multiple votes, illicit voting, and malicious actors that can be exploited electronically to disrupt voting or affect vote counts. However, this research proposed to develop a secure, smart, verifiable e-voting system (SSVEVS) that can offer an authenticated end-to-end tally voting system, a top-secret ballot election system, and confidence in overall election integrity. The proof of this smart and secure electronic voting system utilizes a 64-bit quad-core ARM Cortex-A76 processor, integrated with multimodal biometric (facial and fingerprint) authentication systems, programmed with a deep-learning image processing (DLIP) algorithm to optimize image detection and recognition. Also, an Ethereum blockchain technology (EBT) with a homomorphic encryption algorithm was implemented on the system to ensure that the original information record is maintained, immutable, tamper-resistant, and transparent throughout the electoral process, and stores the information in a decentralized database application. The system achieved 1,424.501 TPS for 10,000 transactions with a mining time of 7.02 seconds. The biometric (facial and fingerprint) authentication achieved a False Acceptance Rate (FAR) and False Rejection Rate (FRR) of 0.00% respectively. The True Acceptance Rate (TAR) is 100%, with a false image template of 0.04% true identification rate (TPIR).

Gaurang Bharadava, Madhav P. Desai, Anshuman Prajapati, Manav Bagthaliya · 5 authors

Advances in digital technology brought about in the recent past by blockchain's transparency, security, and decentralization have changed things.The present paper discusses a blockchain-enabled crowdfunding platform that eliminates fraud, processes that go undocumented, and accountability issues that arise in the existing systems.Automation takes place with the use of Ethereum blockchain and smart contracts.The implementation of the smart contract employs Solidity, testing and deployment is handled through Foundry, and wallet integration is with MetaMask.HTML and JavaScript facilitate user navigation, moving blockchain nearer to the user interface.The platform addresses problems such as latency and complexity, providing a trustless, low-cost funding system.Future updates can incorporate token integration and cross-chain compatibility for increased stability and user adoption.

Hayden Savage, Jennifer Chan

No abstract is available for this record.

Sergii Grybniak, Oleksandr Nashyvan, Igor Mazurok, Yevhen Leonchyk · 5 authors

ABSTRACT This article presents algorithms and smart contract designs to facilitate recurring bill payments on decentralised platforms using the Ethereum‐like Virtual Machine (EVM). The proposed approach enables the inclusion of recurring and deferred transactions, ensuring compatibility with ERC‐20 and ERC‐777 standards, thereby contributing to the establishment of a novel token standard. The research addresses the growing need for non‐custodial mechanisms to support automatic periodic payments in decentralised environments, offering potential benefits to both service providers and customers in various industries.

Xihui Haviour Chen, Provash Kumer Sarker, Chi Keung Marco Lau

No abstract is available for this record.

N.-H. Chen, Jennifer Chan, Linh Nghiem

No abstract is available for this record.

Hye Jin Lee, Duc Anh Luong, Jong Hwan Park, Hyoseung Kim

Performance appraisal is crucial in human resource management to identify areas within organizations. Ensuring anonymity and confidentiality is important to obtain honest feedback and prevent retaliation. Although blockchain-based anonymous reputation systems have been discussed, permissioned blockchains are susceptible to Sybil attack vulnerabilities, while permissionless private blockchains do not provide full anonymity. We present the Anonymous Reputation System for Performance Appraisal (ARSPA), which uses a permissionless public blockchain. This system is designed for upward feedback in performance appraisals, employing cryptographic techniques such as non-interactive zero-knowledge proofs, public key encryption, and Merkle trees to ensure security. Our protocol addresses the risks of Sybil attacks, ensures review limitation and unforgeability. We validate the security of ARSPA through analysis and demonstrate its feasibility through proof-of-concept on Ethereum test networks. ARSPA provides a secure and efficient approach to improve the reliability and fairness of performance appraisal.

Oleg Klym, Tetiana Melnyk

The complexity of business operations in the global economy is rising, necessitating new approaches to contractual relationship management. This article explores the potential of smart contracts as a transformative tool for international business. By utilizing blockchain technology, smart contracts can automate agreements, reduce transaction costs, and enhance security and efficiency. Initially developed on the Ethereum network, smart contracts have since evolved with the introduction of new blockchain platforms. This paper examines the theoretical background of smart contracts, their practical applications in international business activities, and their impact on economic efficiency. Despite high costs of development, insufficient legal frameworks, and trust-related concerns, the findings suggest that smart contracts hold great potential to optimize international economic activities by increasing transparency, reducing risks, and facilitating decentralized governance.

Jobaer Hossain

No abstract is available for this record.

Sumin Li, Rentao Wang, Yudong Wan, Jincheng Hu

The accurate prediction of cryptocurrency prices remains challenging due to their high volatility, which is driven by complex factors including market dynamics, macroeconomic conditions, and investor sentiment. Traditional econometric models, standalone machine learning methods, and deep learning architectures have shown limited effectiveness in capturing both short-term variations and long-range dependencies. To address these limitations, a hybrid deep learning model, L-FED, is proposed by integrating long-short term memory (LSTM) network with the FEDformer architecture, augmented by sentiment analysis. A parallel framework is adopted to enable bidirectional information interaction through local-global collaborative learning. A comprehensive feature engineering approach is also introduced, incorporating historical trading data, technical indicators, sentiment features, and LSTM-derived short-term guiding prices. The experimental results demonstrate that L-FED outperforms the existing baseline models in terms of prediction accuracy. On the Bitcoin and Ethereum datasets, L-FED achieves improvements of 16% and 12.8% in RMSE and MAPE, respectively, for Bitcoin, and 11.6% and 6.4% for Ethereum. Furthermore, sentiment analysis using the CryptoBERT model enhances price prediction accuracy by 19% and 2.9%, respectively, attributable to its pre-training on a large, domain-specific cryptocurrency corpus. Our code and datasets are publicly available at https://github.com/lsm-2024/L-FED.

Amrita Jain, Savi Jain, Shruti Lashkari, Sweta Gupta · 5 authors

The overall objective of the study is to understand the current status of blockchain applications and evaluate their ability to satisfy the growing demand for blockchain knowledge in the applications industry.In order to determine which would be the superior option in each situation, it also evaluated the respective advantages of Ethereum and Hyperledger.The study's extensive data set allowed it to offer priceless insights into the intricate workings of a blockchain application.The materials used included reports, journals, and periodicals.The "smart contract" refers to a digital transaction that runs on its own, logs the pertinent dynamic activity on a distributed ledger, and uses predefined criteria to demonstrate its legitimacy.The key component of a blockchain that enables its use as a platform for use cases beyond currency is a smart contract.Voting, education, entertainment, real estate, the Internet of Things (IoT), The development of blockchain technology has advanced significantly in recent years, with a particular emphasis on smart contracts; yet, little research has been done on the idea.Notwithstanding the many advantages of smart contracts, a number of obstacles have prevented their widespread use, including as security holes, coverage gaps, and the difficulties of lawfully enforcing contracts.

Ricardo Manzano, Marzia Zaman, Darshana Upadhyay, Nishith Goel · 5 authors

The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. This transmission of raw data not only incurs substantial bandwidth costs but also raises significant privacy concerns. In this paper, we propose a federated learning framework for intrusion detection on IoT networks that incorporates a distributed storage system based on the Ethereum blockchain, enhancing the security of the federated learning process. This design offers several key benefits, including scalability, high availability, redundancy, and the capacity to process large datasets. Despite these advantages, relying solely on federated learning may not yield accurate results, particularly when dealing with highly imbalanced datasets. To address this challenge, we have integrated a diffusion model for data augmentation at each local node, which strengthens model robustness. Furthermore, to protect data privacy at each local node, we utilize transmitting and averaging model parameters instead of raw data. The proposed framework is trained and evaluated in two datasets. The MNIST (Modified National Institute of Standards and Technology) dataset and BoT-IoT dataset. Our results indicate significant improvements in detecting zero-day attacks, achieving an average F1-score of 98.3% on the short version of the BoT-IoT dataset as well.