Blockchain Transactions Research Articles

Blockchain-based e-voting system in a university

The blockchain-based electronic voting (e-voting) system, offers universities a safe, easy-to-use platform that enhances accuracy and integrity. Despite that, it is challenging to integrate the blockchain-based e-voting system with current platforms and private data. Managing latency is another requirement during the blockchain transactions (votes/elections). In this work, we suggested a novel system that uses smart contracts on the consortium blockchain to address these constraints. The voters and electors in a university can vote and elect respecting the rules established in smart contracts. The miners validate transactions using proof of work (PoW) and proof of stake (PoS). Data integrity and voter validity are ensured via the SHA-256 hash algorithm and the ECDSA signature. The implementation results demonstrate that the suggested method works better than the state-of-the-art. exceeds the state-of-the-art in terms of gas cost and execution time.

A Geth-based real-time detection system for sandwich attacks in Ethereum

With the rapid development of the Ethereum ecosystem and the increasing applications of decentralized finance (DeFi), the security research of smart contracts and blockchain transactions has attracted more and more attention. In particular, front-running attacks on the Ethereum platform have become a major security concern. These attack strategies exploit the transparency and certainty of the blockchain, enabling attackers to gain unfair economic benefits by manipulating the transaction order. This study proposes a sandwich attack detection system integrated into the go-Ethereum client (Geth). This system, by analyzing transaction data streams, effectively detects and defends against front-running and sandwich attacks. It achieves real-time analysis of transactions within blocks, quickly and effectively identifying abnormal patterns and potential attack behaviors. The system has been optimized for performance, with an average processing time of 0.442 s per block and an accuracy rate of 83%. Response time for real-time detection new blocks is within 5 s, with the majority occurring between 1 and 2 s, which is considered acceptable. Research findings indicate that as a part of the go-Ethereum client, this detection system helps enhance the security of the Ethereum blockchain, contributing to the protection of DeFi users’ private funds and the safety of smart contracts. The primary contribution of this study lies in offering an efficient blockchain transaction monitoring system, capable of accurately detecting sandwich attack transactions within blocks while maintaining normal operation speeds as a full node.

Legitimacy of Smart Contracts from the Perspective of Islamic Law: A Case Study of Blockchain Transactions

Legitimacy of Smart Contracts from the Perspective of Islamic Law: A Case Study of Blockchain Transactions

Disintermediation of consumer services through blockchain? The role of intermediary brands, value-added services, and privacy concerns

This paper explores the dynamics between value-added services, intermediary brands, and consumer privacy concerns in shaping attitudes toward blockchain-enabled consumer services. Grounded in the Antecedents-Privacy Concerns-Outcomes (APCO) framework, we develop a theoretical model that we test in three experimental studies with a total of 1613 participants, utilizing verbal scenarios featuring blockchain applications for international money transfer and hotel booking. Our research reveals that complete disintermediation via pure peer-to-peer blockchain transactions is unlikely. Consumers prefer blockchain applications offering supplementary services like call centers, password assistance, and cancellation options. As consumers become familiar with blockchain technology, privacy concerns intensify due to its distributed and immutable storage. The fears of data breaches are more pronounced when blockchain applications are offered by unknown startups as opposed to well-known Big Tech companies. However, privacy-conscious consumers also value the prospect of distancing themselves from big-data ecosystems by embracing blockchain solutions from startups. Our research extends the APCO framework by clarifying how privacy concerns, brand-based heuristics, and technological attributes interact. For managerial implications, blockchain applications necessitate re-intermediation to meet consumer preferences. Potential intermediaries, including Big Tech firms, startups, and industry incumbents, face unique challenges in developing and marketing blockchain-enabled consumer services.

Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis

As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating explainable artificial intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The shapley additive explanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we introduce an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances true positive rate (TPR) and receiver operating characteristic-area under curve (ROC-AUC) scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and false positive rate (FPR) scores.

Reentrancy vulnerability detection based on graph convolutional networks and expert patterns under subspace mapping

Smart contracts with automatic execution capability provide a vast development space for transactions in Blockchain. However, due to the vulnerabilities in smart contracts, Blockchain has suffered huge economic losses, which greatly undermines people’s trust in Blockchain and smart contracts. In this paper, we explore a vulnerability detection method based on graph neural networks and combine both contract source code and opcode. The structure of the method consists of four modules, i.e., preprocessing, subspace mapping, feature extraction, and detection modules. In the feature mapping module, we use a multi-subspace mapping approach to explore the impact of different subspace mappings on the detection method. For reentrancy vulnerability, we conducted extensive experiments. The experiments prove that our method achieves 95% accuracy and 94% F1-Score on average.

Enhancing Global Blockchain Privacy via a Digital Mutual Trust Mechanism

Blockchain technology, initially developed as a decentralized and transparent mechanism for recording transactions, faces significant privacy challenges due to its inherent transparency, exposing sensitive transaction data to all network participants. This study proposes a blockchain privacy protection algorithm that employs a digital mutual trust mechanism integrated with advanced cryptographic techniques to enhance privacy and security in blockchain transactions. The contribution includes the development of a new dynamic Byzantine consensus algorithm within the Practical Byzantine Fault Tolerance framework, incorporating an authorization mechanism from the reputation model and a proof consensus algorithm for robust digital mutual trust. Additionally, the refinement of homomorphic cryptography using the approximate greatest common divisor technique optimizes the encryption process to support complex operations securely. The integration of a smart contract system facilitates automatic and private transaction execution across the blockchain network. Experimental evidence demonstrates the superior performance of the algorithm in handling privacy requests and transaction receipts with reduced delays and increased accuracy, marking a significant improvement over existing methods.

Anomaly Detection in Blockchain Using Machine Learning

Blockchain technology has gained significant attention as a secure and decentralized platform for various applications. However, the immutable and distributed nature of blockchain also presents unique challenges for detecting anomalies and suspicious activities within the network. This research paper proposes a novel approach to anomaly detection in blockchain using machine learning techniques. The goal of this study is to develop an effective and scalable anomaly detection framework that can analyze the vast amount of data generated within a blockchain network and identify irregularities or potential security threats. The proposed framework leverages the power of machine learning algorithms to learn patterns, relationships, and behaviours from historical blockchain data, enabling the detection of anomalous activities in real time.The research paper first focuses on feature extraction techniques tailored specifically for blockchain data. These techniques consider key characteristics of blockchain transactions, such as transaction size, timestamp, and involved addresses, to construct meaningful features that capture the underlying patterns and trends. Various dimensionality reduction techniques are also explored to handle the high-dimensional nature of blockchain data.Subsequently, several machine learning algorithms, including clustering, classification, and anomaly detection methods, are employed to train models using the extracted features. The performance of different algorithms is evaluated using benchmark datasets and real-world blockchain data to assess their accuracy, precision, and recall in detecting anomalies. Additionally, the scalability of the proposed framework is investigated to ensure its effectiveness in large-scale blockchain networks.Furthermore, the research paper investigates the integration of domain-specific knowledge, such as known attack patterns and regulatory compliance rules, into the anomaly detection framework. This hybrid approach combines the strengths of machine learning algorithms with expert knowledge to enhance the accuracy and interpretability of anomaly detection results.The experimental results demonstrate that the proposed anomaly detection framework achieves promising performance in identifying various types of anomalies in blockchain data. It exhibits high detection rates while minimizing false positives, thereby providing valuable insights for blockchain network administrators and regulators to mitigate security risks and safeguard the integrity of blockchain systems. In conclusion, this research paper presents an innovative approach to anomaly detection in blockchain using machine learning. The proposed framework addresses the unique challenges posed by blockchain's decentralized and immutable nature, offering an effective solution for detecting suspicious activities and ensuring the security of blockchain networks. The findings of this study contribute to the growing field of blockchain analytics and have significant implications for real-world blockchain applications in domains such as finance, supply chain management, and healthcare.

ОСОБЛИВОСТІ ВИКОРИСТАННЯ ТЕХНОЛОГІЇ БЛОКЧЕЙН У СФЕРІ УПРАВЛІННЯ ІНТЕЛЕКТУАЛЬНОЮ ВЛАСНІСТЮ

The article discusses the current view on the use of blockchain technology in the field of intellectual property management. Today, blockchain is actively used in various business areas, mainly to improve business processes and reduce costs. The main advantages of blockchain are related to the elimination of intermediaries, data reliability, trust and transparency. Storing transaction information without the need for third-party involvement allows transactions to be completed faster and at lower costs. In today's intellectual property system, licensing, identification of the right holder, investigation of intellectual property rights infringements, and other actions related to the management of intellectual property rights are mostly performed by third parties. Blockchain eliminates the need for such intermediaries, which reduces the cost of managing intellectual property rights. In addition to all of the above, there are a number of potential problems associated with the technical specifics of the blockchain: immutability of the entered data; blockchain is the subject of controversy due to high energy consumption associated with "mining"; most of the existing publicly available consensus mechanisms in blockchain systems rely on the presence of cryptocurrency in the system, which makes it difficult to develop applications not related to the concept of cryptocurrency; the lack of cryptocurrency-independent consensus mechanisms for public blockchains makes it difficult to separate them from "digital coins". There are also problems with using blockchain that are not specific to the intellectual property sector: the speed of processing blockchain transactions is significantly slower than traditional transaction platforms such as VISA or PayPal); lack of proper regulatory support in the blockchain sector; lack of planning for legal requirements on early blockchain platforms.

A Review of Bitcoin Price Prediction Based on Deep Learning Algorithms

This study provides a comprehensive analysis of the existing body of work on predicting the price of Bitcoin using deep learning techniques. It discusses the fundamental concepts behind deep learning and Bitcoin, including recurrent neural networks, convolutional neural networks, and long short-term memory networks. The study also examines the data sources used in training these models, including historical Blockchain transaction data, social media sentiments, and Bitcoin prices. The report also highlights the importance of metrics like mean absolute error, mean squared error, and root mean squared error for evaluating the effectiveness of various models. It also discusses future research topics, such as incorporating external factors into prediction models. The article offers valuable insights for academics, practitioners, and policymakers interested in cryptocurrency prediction.

Enhancing health-care security: The role of blockchain and consensus mechanisms

Blockchain technology has gained prominence recently by virtue of its strong security features for clinical data. Automation of blockchain transactions enables data transactions and records, providing decentralized, secure, and dependable access. Through intelligence-sharing agreements, it can also manage member relationships without the need for a middleman or other third party. Researchers in the health-care industry using blockchain algorithms to safeguard security of data, which is properly stored, are on the rise. In addition, this technology is patient adaptive. Patients and other health-care users can now trust the technology because it prevents any third party from accessing the medical data. Many platforms intended for use in the health-care domain are emerging, including Gem Health Network and MedRec. Using blockchain in health-care protects user data and grants them full authority over their data. However, blockchain technology is also confronting challenges and limitations regarding data privacy and storage capacity. This paper explores the implementation of blockchain within health-care sector while providing an overview of this technology and the different consensus algorithms used in blockchain technology.

PRIBANI: a privacy-ensured framework for blockchain transactions with information retrieval

PRIBANI: a privacy-ensured framework for blockchain transactions with information retrieval

Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning

To address the challenge of balancing privacy protection with regulatory oversight in blockchain transactions, we propose a regulatable privacy protection scheme for blockchain transactions. Our scheme utilizes probabilistic public-key encryption to obscure the true identities of blockchain transaction participants. By integrating commitment schemes and zero-knowledge proof techniques with deep learning graph neural network technology, it provides privacy protection and regulatory analysis of blockchain transaction data. This approach not only prevents the leakage of sensitive transaction information, but also achieves regulatory capabilities at both macro and micro levels, ensuring the verification of the legality of transactions. By adopting an identity-based encryption system, regulatory bodies can conduct personalized supervision of blockchain transactions without storing users’ actual identities and key data, significantly reducing storage computation and key management burdens. Our scheme is independent of any particular consensus mechanism and can be applied to current blockchain technologies. Simulation experiments and complexity analysis demonstrate the practicality of the scheme.

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

INTRODUCTION: The implementation of robust security protocols is imperative in light of the exponential growth of blockchain-based platforms such as Ethereum. The importance of developing more effective strategies to detect and counter potential attacks is growing in tandem with the sophistication of the methods employed by attackers. In this study, we present a novel approach that leverages quantum computing to identify and predict attack vectors on the Ethereum blockchain. OBJECTIVES: The primary objective of this study is to suggest an innovative methodology for enhancing the security of Ethereum by leveraging quantum computing. The purpose of this study is to demonstrate that QRBM and QDN are efficient in identifying and predicting security flaws in blockchain transactions. METHODS: We combined methods from quantum computing with social network research approaches. An enormous dataset containing both genuine Ethereum transactions and a carefully chosen spectrum of malicious activity indicative of popular attack vectors was used to train our model, the QRBM. Thanks to the dataset, the QRBM was able to learn to distinguish between typical and out-of-the-ordinary activities. RESULTS: In comparison to more conventional deep learning models, the QRBM showed substantially better accuracy when it came to identifying transaction behaviours. The model's improved scalability and efficiency were made possible by its quantum nature, which is defined by features like entanglement and superposition. Specifically, the QRBM handled non-informative inputs better and solved problems faster. CONCLUSION: This study paves the way for further investigation into quantum-enhanced cybersecurity measures and highlights the promise of quantum neural networks in strengthening the security of blockchain technology. According to our research, quantum computing has the potential to be an essential tool in creating Ethereum-style blockchain security systems that are more advanced, efficient, and resilient.

Proxy Re-Encryption for Secure Data Sharing with Blockchain in Internet of Medical Things

The Internet of Medical Things (IoMT) is crucial in our daily health monitoring and assisting doctors in treatment. However, the existing schemes mainly use cloud servers to solve the storage of large amounts of data generated by smart devices, ignoring the guarantee mechanism of ciphertext integrity, which may result in data being tampered with or replaced in data sharing. To address this issue, we propose a secure data sharing scheme named Proxy Re-Encryption for Secure Data Sharing with Blockchain in Internet of Medical Things (PRE-IoMT). Specifically, we propose a new proxy re-encryption data sharing approach by introducing identity hash during the key generation phase to realize the binding of public key and user identity, thus enhancing the security of data sharing in PRE-IoMT. Moreover, we adopt BlockChain transactions to design a pairing function ciphertext check mechanism, which can effectively prevent the ciphertext stored in the cloud server from being tampered with. We add the user to the accumulator by invoking the BlockChain smart contract to realize the management and fast query of users. Finally, we conduct the performance analysis experiments on the four-stage computation of proxy re-encryption, communication overhead, storage overhead, transmission delay, and the delay of BlockChain transactions, compared with existing schemes, our scheme improves the computation efficiency of encryption, re-encryption, decryption, and re-decryption by about 23.8%, 71.4%, 48%, and 15.3% respectively. The security proof also exhibits that PRE-IoMT can resist IND-ID-CPA.

Research on blockchain abnormal transaction detection technology combining CNN and transformer structure

With the rapid development of blockchain and digital currencies, cybercrimes targeting blockchain transactions are proliferating. Various abnormal transaction behaviors caused by malicious nodes and malicious transactions have emerged in blockchain systems. Therefore, blockchain transaction security research is a hot issue in blockchain research. Deep learning has made remarkable achievements in image and audio. However, deep learning is less effective when processing text-like data than tree-based models. Therefore, for the highly unbalanced textual financial dataset, this paper designs a deep learning anomaly detection method called 1D SA-Inception by combining CNN with Transformer structure on the basis of traditional one-dimensional CNN (1D-CNN), introduces the Inception structure to learn the information of different scales of tabular data, and introduces the Transformer (self-attention) self-attention mechanism for the research of blockchain anomaly transaction detection technology, effectively improving the prediction effect. Finally, by conducting experiments on the extreme class imbalance dataset, the results show that the 1D SA-Inception designed in this paper outperforms the traditional CNN with AUC, G-mean, and F1 indexes of 91.05 %, 84.95 %, and 83.52 %, respectively, on the textual dataset. This method can more efficiently achieve the detection of abnormal transaction behavior, safeguard consumer rights and interests, and ensure the safe development of the financial sector.

R-GCN: a residual-gated recurrent unit convolution network model for anomaly detection in blockchain transactions

R-GCN: a residual-gated recurrent unit convolution network model for anomaly detection in blockchain transactions

SHA 512 Enhanced Secure Hash Decentralized Response System

Today a very huge lack of confidence about the current EVM/ballot response system like voting has made voting response collection in any democratic country very condemnatory. People have watched their fundamental rights which are being violated. On the other hand the other digital voting systems are also in danger due to a minimal of transparency. Most of the traditional response systems are not clear enough so as to meet today's requirements; this crucial reason makes it very sweaty for the government to come up with voters' trust. The motive behind the downfall of the traditional and current cyber voting system is that it can be easily compromised due to centralized network. The primary objective here is to counter the issues of the traditional digital voting and such response systems, which has any type of mistrust or inequality during the process of accepting responses through response systems like voting. Blockchain technology can be used in such response systems like voting to have a fair response system and reduce wrongdoing. The current physical response systems like voting have many downs in it as well as the digital voting systems are not up to the mark to be implemented on a huge scale. Such all problems raises the need for a solution to secure the democratic rights of the people. This article put-ups a framework based on up-to-date blockchain algorithm technology that provides high clarity and reliability of the system to construct a healthy relationship between response systems and election authorities. The insisted software provides a framework that can be implemented in real world to conduct voting like procedures digitally through blockchain rather than involving any physical polling stations and centralized ledger. Our proposed framework provides an extendible hashing, by using the latest consensus algorithms. The Secure Hash Algorithms applied in the voting system along with Chain Security makes the response systems more secure. Distributed ledger contracts also provide a protected connection between the user and the consensus network while running a transaction in the chain. The security of the blockchain based response system has also been discussed. In Addition to this, encryption of responses using cryptographic hash functions and prevention of exploitation attacks about 51% on the blockchain has also been elaborated. Also, the procedure for carrying out blockchain transactions during the process of response collection has been elaborated using Blockchain. Finally, the throughput performance evaluation of the proposed system shows that such a system can be deployed in a large-scale real time population.

A Blockchain-Based Fairness Guarantee Approach for Privacy-Preserving Collaborative Training in Computing Force Network

The advent of the big data era has brought unprecedented data demands. The integration of computing resources with network resources in the computing force network enables the possibility of distributed collaborative training. However, unencrypted collaborative training is vulnerable to threats such as gradient inversion attacks and model theft. To address this issue, the data in collaborative training are usually protected by cryptographic methods. However, the semantic meaninglessness of encrypted data makes it difficult to prevent potential data poisoning attacks and free-riding attacks. In this paper, we propose a fairness guarantee approach for privacy-preserving collaborative training, employing blockchain technology to enable participants to share data and exclude potential violators from normal users. We utilize a cryptography-based secure aggregation method to prevent data leakage during blockchain transactions, and employ a contribution evaluation method for encrypted data to prevent data poisoning and free-riding attacks. Additionally, utilizing Shamir’s secret sharing for secret key negotiation within the group, the negotiated key is directly introduced as noise into the model, ensuring the encryption process is computationally lightweight. Decryption is efficiently achieved through the aggregation of encrypted models within the group, without incurring additional computational costs, thereby enhancing the computational efficiency of the encryption and decryption processes. Finally, the experimental results demonstrate the effectiveness and efficiency of our proposed approach.

LEGAL IMPLICATIONS OF BLOCKCHAIN TECHNOLOGY FOR TAX COMPLIANCE AND FINANCIAL REGULATION

Blockchain technology has emerged as a disruptive force in the realms of tax compliance and financial regulation, presenting both opportunities and challenges for governments, businesses, and regulators worldwide. This abstract explores the multifaceted legal implications stemming from the integration of blockchain technology into tax systems and financial frameworks. The decentralization and transparency inherent in blockchain networks offer promising avenues for enhancing tax compliance. Smart contracts, powered by blockchain, can automate tax calculations and payments, reducing errors and facilitating real-time monitoring of transactions. Additionally, the immutable nature of blockchain ledgers provides auditors with an unprecedented level of transparency and traceability, potentially reducing tax evasion and fraud. However, the adoption of blockchain technology also poses significant regulatory challenges. The anonymity afforded by certain blockchain implementations raises concerns regarding the identification and verification of taxpayers and transactions, potentially hindering enforcement efforts. Moreover, the cross-border nature of blockchain transactions complicates traditional tax jurisdictional boundaries, necessitating international cooperation and harmonization of tax policies. In the realm of financial regulation, blockchain technology introduces novel considerations for regulators seeking to ensure market integrity and investor protection. The proliferation of blockchain-based financial products, such as cryptocurrencies and tokenized assets, challenges existing regulatory frameworks designed for traditional financial instruments. Regulators must grapple with issues of investor disclosure, market manipulation, and systemic risk in this rapidly evolving landscape. Furthermore, the decentralized nature of blockchain networks challenges the efficacy of traditional regulatory mechanisms, such as centralized oversight and enforcement. Regulators face the daunting task of striking a balance between fostering innovation and safeguarding against potential risks, such as money laundering and terrorist financing, inherent in decentralized financial systems. The integration of blockchain technology into tax compliance and financial regulation presents a complex array of legal implications. While offering potential benefits in terms of efficiency and transparency, blockchain also necessitates adaptation and evolution of regulatory frameworks to address emerging challenges and risks in a rapidly evolving digital landscape. Effective collaboration between governments, businesses, and regulators is essential to harness the transformative potential of blockchain technology while mitigating its associated legal and regulatory challenges.
 Keywords: Blockchain, Regulators, Legal, Tax, Technology, Financial, Review.