Blockchain-Research and Applications最新文献

Corruption and lack of transparency remain critical challenges in governance systems around the world. These issues are often perpetuated by centralized systems and their manipulation by system administrators. Furthermore, the lack of data ownership and the monetization of user data by tech companies further increase concerns about transparency. In light of these concerns, this study aims to review existing blockchain-based governance models and identify best-practice governance models focusing on corruption transparency, their characteristics, and components. The research will also examine the role of a token economy in addressing trusted third-party issues related to asset ownership management. Furthermore, we discuss the effect of smart contracts, blockchain, decentralized autonomous organizations (DAO), Web 3.0, and multifactor challenge set self-sovereign identity authentication (MFSSIA) as modern technologies to combat corruption and achieve transparency in the public sector. To achieve this, we conduct a systematic literature review (SLR) comprising peer-reviewed journals, proceedings, and book chapters published between 2012 and 2023. Using the SLR methodology, 45 primary and supporting studies have been selected for result extraction and analysis. Finally, we discovered seven blockchain-based governance models with their characteristics and primary components.

We study Privacy Pools, a novel smart contract-based privacy-enhancing protocol. The protocol introduces a mechanism for users to reveal certain properties of their transaction without having to reveal the transaction itself. The core concept involves allowing users to publish a zero-knowledge proof, demonstrating that their funds (do not) originate from known (un-)lawful sources, without publicly revealing their entire transaction history. This is achieved by proving membership in custom association sets, which are designed to demonstrate compliance with regulatory frameworks or social consensus. We illustrate how this mechanism can create a separating equilibrium between compliant and non-compliant withdrawals. Our work describes the technical underpinnings, incentives, and broader implications of this mechanism, highlighting how Privacy Pools-like protocols can create more private yet compliant blockchain transactions.

In recent years, blockchain technologies have expanded from the finance field to other areas that rely on trust-based solutions. The healthcare industry represents one such area, as digital transformation disrupts relationships between patients, healthcare professionals, and healthcare institutes. Patients and healthcare institutes lack a proficient tool to verify the credentials of medical professionals in a digital environment. Furthermore, healthcare professionals lack a tool where they are in control over their credentials. The first contribution of this paper is a proposal of a solution that leverages the private permissioned Hyperledger Fabric blockchain and smart contracts to provide a source of transparent trust for relationships within the healthcare industry. Second, we pave the ground for GDPR compliance by storing only the hash values on the blockchain. Third, we solve the problem of patient authentication by utilizing cryptographic techniques. Finally, we prove the usability of the proposed solution by implementing a user interface and creating a live deployment.

The recent surge of Ethereum in prominence has made it an attractive target for various kinds of crypto crimes. Phishing scams, for example, are an increasingly prevalent cybercrime in which malicious users attempt to steal funds from a user's crypto wallet. This research investigates the effects of network architectural features as well as the temporal aspects of user activities on the performance of detecting phishing users on the Ethereum transaction network. We employ traditional machine learning algorithms to evaluate our model on real-world Ethereum transaction data. The experimental results demonstrate that our proposed features identify phishing accounts efficiently and outperform the baseline models by 4% in Recall and 5% in F1-score.

The valuation of cryptocurrencies is important given the increasing significance of this potential asset class. However, most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources. In this study, a robust cryptocurrency valuation method is developed using up-to-date datasets. Using various panel regression models and moving-window regression tests, the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1, 2009 to April 30, 2023. The research shows the importance of sentiments and suggests that the fear and greed index can indicate when to make a cryptocurrency investment, while Google search interest in cryptocurrency is crucial when choosing the appropriate type of cryptocurrency. Moreover, consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins, while their impacts on cryptocurrencies with stablecoins are insignificant. Other fundamental factors, such as the type of supply and the presence of smart contracts, do not have a significant influence on cryptocurrency. Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors, portfolio managers, and policymakers in assessing the utility level of each cryptocurrency.

Federated Learning (FL) is a type of distributed deep learning framework in which multiple devices train a local model using local data, and the gradients of the local model are then sent to a central server that aggregates them to create a global model. This type of framework is ideal where data privacy is of utmost importance because the data never leave the local device. However, a major concern in FL is ensuring the data quality of local training data. Since there is no control over the local training data, ensuring that the local model is trained on clean data becomes challenging. A model trained on poor-quality data can have a significant impact on its accuracy. In this paper, we propose a decentralized approach using blockchain to ensure local model data quality. We use miners to validate each local model by checking its accuracy against a secret testing dataset. This is done using a smart contract that the miners invoke during the mining process. The local model is aggregated with the global model only if it passes a preset accuracy threshold. We test our proposed method on two datasets: the Brain Tumor Classification dataset from Kaggle, comprised of 7000 MRI images divided into two classes (Tumor/No Tumor), and the Medical MNIST dataset, which includes 58,954 images classified into six different classes: AbdomenCT, BreastMRI, ChestCT, Chest X-ray, Hand X-ray, and HeadCT. Our results show that our method outperforms the original FL approach in all experiments.

The rapid growth of the Internet of Things (IoT) has raised security concerns, including MQTT protocol-based applications that lack built-in security features and rely on resource-intensive Transport Layer Security (TLS) protocols. This paper presents an approach that utilizes blockchain technology to enhance the security of MQTT communication while maintaining efficiency. This approach involves using blockchain sharding, which enables higher scalability, improved performance, and reduced computational overhead compared to traditional blockchain approaches, making it well-suited for resource-constrained IoT environments. This approach leverages Ethereum blockchain's smart contract mechanism to ensure trust, accountability, and user privacy. Specifically, we introduce a shard-based consensus mechanism that enables improved security while minimizing computational overhead. We also provide a user-controlled and secured algorithm using Proof-of-Access implementation to decentralize user access control to data stored in the blockchain network. The proposed approach is analyzed for usability, including metrics such as bandwidth consumption, CPU usage, memory usage, delay, access time, storage time, and jitter, which are essential for IoT application requirements. The analysis demonstrated that the approach reduces resource consumption, and the proposed system outperforms TLS and existing blockchain approaches in these metrics, regardless of the choice of the MQTT broker. Additionally, thoroughly addressing future research directions, including issues and challenges, ensures careful consideration of potential advancements in this domain.

Since the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism can avoid the performance bottleneck of blockchain systems caused by Proof of Work (PoW), it has been widely used in many scenarios. However, in the blockchain system, each node is required to back up all transactions and block data of the system, which will waste a lot of storage resources. It is difficult to apply to scenarios with limited storage resources such as unmanned aerial vehicle networks and smart security protection; thus, it is often used in small-scale networks. In order to deploy PBFT-based blockchain systems in large-scale network scenarios, we propose an ultra-low storage overhead PBFT consensus (ULS-PBFT), which groups nodes hierarchically to limit the storage overhead within the group. In this paper, we first propose an optimal double-layer PBFT consensus from the perspective of minimizing the storage overhead, and prove that this consensus can significantly reduce the storage overhead. In addition, we also investigate the superiority of ULS-PBFT in terms of communication overhead while setting the security threshold in the presence of the possibility of Byzantine nodes. The simulation results demonstrate the advantages of ULS-PBFT. Then, we extend such grouping idea to the blockchain system with X-layer PBFT and analyze its storage and communication overhead. Finally, the node grouping strategy of double-layer PBFT is studied for four application scenarios when the performance of storage overhead, communication overhead, and security are considered comprehensively.

The modern pharmaceutical supply chain lacks transparency and traceability, resulting in alarming rates of counterfeit products entering the market. These illegitimate products cause harm to end users and wreak havoc on the supply chain itself, costing billions of dollars in profit loss. In this paper, in response to the Drug Supply Chain Security Act (DSCSA), we introduce Janus, a novel pharmaceutical track-and-trace system that utilizes blockchain and cloning-resistant hologram tags to prevent counterfeits from entering the pharmaceutical supply chain. We design a multi-quorum consensus protocol that achieves load balancing across the network. We perform a security analysis to show robustness against various threats and attacks. The implementation of Janus proves that the system is fair, scalable, and resilient.