Byzantine Fault Tolerance Algorithm Research Articles

Blockchain-based lightweight trusted data interaction scheme for cross-domain IIoT

To facilitate cross-domain data interaction in the Industrial Internet of Things (IIoT), establishing trust between multiple administrative domains is essential. Although blockchain technology has been proposed as a solution, current techniques still suffer from issues related to efficiency, security, and privacy. Our research aims to address these challenges by proposing a lightweight, trusted data interaction scheme based on blockchain, which reduces redundant interactions among entities. We enhance the traditional Practical Byzantine Fault Tolerance (PBFT) algorithm to support lightweight distributed consensus in large-scale IIoT scenarios. Introducing a composite digital signature algorithm and incorporating veto power minimizes resource consumption and eliminates ineffective consensus operations. The experimental results show that, compared with PBFT, our scheme reduces latency by 27.2%, thereby improving communication efficiency and resource utilization. Furthermore, we develop a lightweight authentication technique specifically for cross-domain IIoT, leveraging blockchain technology to achieve distributed collaborative authentication. The performance comparisons indicate that our method significantly outperforms traditional schemes, with an average authentication latency of approximately 151 milliseconds. Additionally, we introduce a trusted federated learning (FL) algorithm that ensures comprehensive trust assessments for devices across different domains while protecting data privacy. Extensive simulations and experiments validate the reliability of our approach.

Electronic Health Records Sharing Based on Consortium Blockchain.

In recent years, Electronic health records (EHR) has gradually become the mainstream in the healthcare field. However, due to the fact that EHR systems are provided by different vendors, data is dispersed and stored, which leads to the phenomenon of data silos, making medical information too fragmented and bringing some challenges to current medical services. Therefore, in view of the difficulties in sharing EHR between medical institutions, the risk of privacy leakage, and the lack of EHR usage control by patients, an EHR sharing model based on consortium blockchain is proposed in this paper. Firstly, the Interplanetary File System is combined with consortium blockchain, which forms a hybrid storage scheme of EHR, this technology effectively improves data security, privacy protection, and operational efficiency. Secondly, the model combines unidirectional multi-hop conditional proxy re-encryption based on type and identity with distributed key generation technology to achieve secure EHR sharing with fine grained control. At the same time, users are required to link the operation records of EHR, so as to realize the traceability of EHR usage. A dynamic Byzantine fault-tolerant algorithm based on reputation and clustering is then proposed to solve the problems of arbitrary master node selection, high latency and low throughput of PBFT, enabling the nodes to reach consensus more efficiently. Finally, the model is analyzed in terms of security and user control, showing that the model is less energy intensive in terms of communication overhead and time consumption, and can effectively achieve secure sharing between medical data.

Investigation into Safety Regulation of Entertainment Venues Based on Big Data Label Analysis

The recreational escape rooms have recently emerged as a rapidly growing and widely embraced form of consumer entertainment. However, the industry’s expansion has brought forth certain challenges, notably the lack of authoritative oversight, which has led to issues such as piracy and theme infringement. To address these concerns, this study explores the management complexities of immersive entertainment venues from the perspective of responsive regulation. The study begins by abstracting a dataset of online cultural products related to entertainment venues into a complex network using specific measurement standards. A method employing the K-means clustering algorithm is then proposed to partition the associative structure of this complex network. The K-means clustering algorithm is particularly suitable for this task due to its efficiency in handling large-scale data, strong scalability, and ability to quickly and effectively group network nodes based on a defined objective function. Additionally, the algorithm allows for flexible adjustment of the number of clusters according to specific needs. Furthermore, the study enhances and tests the Practical Byzantine Fault Tolerance (PBFT) algorithm to validate its effectiveness and practicality. The findings reveal that when the distance criterion value is set at a=2, the improved PBFT algorithm exhibits exceptional performance. This discovery significantly enhances the security and control measures in immersive entertainment venues, enriching the theoretical framework related to administrative regulation and providing crucial theoretical resources for future legislative efforts. Finally, the study presents policy recommendations to strengthen the regulation of immersive entertainment venues. This study offers effective technical support for optimizing consumer market management measures and contributes to the development of new entertainment venues.

A Prototype Model of Zero Trust Architecture Blockchain with EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage Decentralized Clinical Trials

The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done for immutability, privacy and security, mutual consensus, transparency, accountability, tracking and tracing, and temperature‒humidity control parameters.

Design of knowledge transaction protection mechanism in the open innovation community based on blockchain technology

Open innovation communities (OICs) are important for the development of modern enterprises. However, there are still many safety hazards and trust crises on community platforms. This paper proposes an approach that combines blockchain technology and an OIC, as blockchain technology provides a direction for solving trust and security problems in community platform transactions. Moreover, the design of smart contracts can ensure the implementation of transactions and improve their reliability. In this study, the participants in an OIC and three types of infringement problems were analyzed. The Byzantine Fault Tolerant (BFT) algorithm was applied to design the logic of smart contracts and the coupling between idea platforms and the blockchain. The protection mechanism of an OIC was established based on blockchain smart contract supervision to provide a better knowledge co-creation environment for OICs.

Dynamic Credible Spectrum Sharing Based on Smart Contract in Vehicular Networks

With the rapid development of the Internet of Vehicles (IoV), the demand for wireless spectrum resources has significantly increased. Dynamic spectrum sharing technology is regarded as a key solution to alleviate the shortage of spectrum resources. However, during the spectrum sharing process, security issues and a low utilization of the shared spectrum may arise. This study designs a consortium blockchain for trustworthy dynamic spectrum sharing in IoV environments. An improved asynchronous byzantine fault-tolerant algorithm is also designed to address the instability of signals in this scenario, and the allocation and management of spectrum resources between vehicles and base stations are further optimized using the Stackelberg game, ultimately deployed automatically through smart contracts. Simulation results show that our method not only significantly improves the system’s response time but also ensures communication quality and can maintain efficient operation under high network delay and complex scenarios.

Enterprise financial sharing and risk identification model combining recurrent neural networks with transformer model supported by blockchain

The objective of this study is to investigate methodologies concerning enterprise financial sharing and risk identification to mitigate concerns associated with the sharing and safeguarding of financial data. Initially, the analysis examines security vulnerabilities inherent in conventional financial information sharing practices. Subsequently, blockchain technology is introduced to transition various entity nodes within centralized enterprise financial networks into a decentralized blockchain framework, culminating in the formulation of a blockchain-based model for enterprise financial data sharing. Concurrently, the study integrates the Bi-directional Long Short-Term Memory (BiLSTM) algorithm with the transformer model, presenting an enterprise financial risk identification model referred to as the BiLSTM-fused transformer model. This model amalgamates multimodal sequence modeling with comprehensive understanding of both textual and visual data. It stratifies financial values into levels 1 to 5, where level 1 signifies the most favorable financial condition, followed by relatively good (level 2), average (level 3), high risk (level 4), and severe risk (level 5). Subsequent to model construction, experimental analysis is conducted, revealing that, in comparison to the Byzantine Fault Tolerance (BFT) algorithm mechanism, the proposed model achieves a throughput exceeding 80 with a node count of 146. Both data message leakage and average packet loss rates remain below 10 %. Moreover, when juxtaposed with the recurrent neural networks (RNNs) algorithm, this model demonstrates a risk identification accuracy surpassing 94 %, an AUC value exceeding 0.95, and a reduction in the time required for risk identification by approximately 10 s. Consequently, this study facilitates the more precise and efficient identification of potential risks, thereby furnishing crucial support for enterprise risk management and strategic decision-making endeavors.

Artwork Traceability and Anti-Counterfeiting Model Based on Block Chain Technology

The Chinese art market fostered the advancement of culture and spirituality. Nevertheless, false and counterfeit artwork is not uncommon. The art technology and security introduce the innovative method to determine Artwork Traceability and Anti-Counterfeiting. The aim of the work is to prevent the creation and circulation of fake or unauthorized copies of artworks. Because of the cryptographic security and immutability of block chains, it would be challenging to falsify artwork records or make accurate duplicates. This would foster confidence in the art market and shield collectors and artists from monetary losses and harm to their reputations. The data on mobile Application Data Center (ADC) platform and the cleansing the data using sub-aperture keystone transform matched filtering (SAKTMF) and the preprocessed data is fed into the Hierarchically Gated Recurrent Neural Network (HGRNN) classify the art work and Anti-Counterfeiting. The Bald Eagle Search Optimization (BES) Algorithm is used to optimize the weight parameter of HGRNN. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such as Dual-Branch Multi-Scale Feature Fusion network (DMF-Net), Region Convolutional Neural Network (R-CNN) and practical Byzantine fault tolerance (PBFT) algorithm. The gained results of the proposed HGRNN-BES method attains higher accuracy 97%, 96%, and 99%, higher precision 98%, 99%, and 97%.

CE-PBFT: An Optimized PBFT Consensus Algorithm for Microgrid Power Trading

Currently, in the blockchain-based distributed microgrid trading system, there are some problems, such as low throughput, high delay, and a high communication overhead. To this end, an improved Practical Byzantine Fault Tolerance (PBFT) algorithm (CE-PBFT) suitable for microgrid power trading is proposed. First, a node credit value calculation model is introduced, and nodes are divided into consensus, supervisory, and propagation nodes according to their credit values, forming a hierarchical network structure to ensure the efficiency and reliability of consensus. Secondly, the consensus process is optimized by adopting a segmented consensus mechanism. This approach calculates the consensus rounds for nodes and selects the methods for node-type switching and consensus based on these calculations, reaching dynamic changes in node states and credit values, effectively reducing the communication overhead of node consensus. Finally, the experiments show that compared with the IMPBFT and PBFT algorithms, the CE-PBFT algorithm has better performance in throughput, delay, and communication overhead, with a 22% higher average throughput and 15% lower average delay than the IMPBFT algorithm and a 118% higher average throughput and 87% lower average delay than the PBFT algorithm.

A blockchain-based data storage architecture for Internet of Vehicles: Delay-aware consensus and data query algorithms

The recent development of the Internet of Vehicles (IoV) relies heavily on the secure store and analysis of reliable driving data. Blockchain technology has been widely used in the IoV field due to its security advantages, where consensus and query algorithms are two key modules determining its performance. However, existing solutions do not perform well enough in terms of delay performance, thus limiting their ability to support IoV environments with ultra-low-latency requirements. To address this challenge, we design a blockchain-based data storage architecture, focusing on the design of delay-aware consensus and data query algorithms. Specifically, to reduce the consensus delay, we propose a reputation-assisted and grouping-simplified Practical Byzantine Fault Tolerant (RGS-PBFT) algorithm. First, we develop a novel node credit value evaluation model by combining the behavior and configuration reputation to select the primary node with both high security and high computing capacity. Second, after determining the primary node, we reduce the communication complexity of traditional PBFT from O(Z2) to O(Z43) by establishing a grouping-simplified consensus structure. These contributions effectively reduce the consensus delay. To reduce the query delay and index construction time, we propose an index query algorithm based on the data tracking chain (DTC-Index query). By introducing the LevelDB database, it establishes a transaction index chain coupled to vehicle ID in the blockchain. This approach avoids the time-consuming block traversal during data querying. Experimental results demonstrate that the proposed algorithms outperform existing solutions, achieving significant improvements in both consensus delay and query time reduction. Notably, our solutions offer exceptional delay performance, making them well-suited for IoV environments.

CG-PBFT: an efficient PBFT algorithm based on credit grouping

Because of its excellent properties of fault tolerance, efficiency and availability, the practical Byzantine fault tolerance (PBFT) algorithm has become the mainstream consensus algorithm in blockchain. However, current PBFT algorithms have problems such as inadequate security of primary node selection, high communication overhead and network delay in the process of consensus. To address these problems, we design a novel efficient Byzantine fault tolerance algorithm based on credit grouping, called CG-PBFT. First, we propose a new credit evaluation model to obtain nodes’ credit values and introduce an optimized three-way quick sorting algorithm to divide nodes into the master-node group, the consensus-node group and the observation-node group, which have different privileges. The nodes in the observation-node group are restricted from participating in consensus, which reduces the communication overhead and improves consensus efficiency. Second, we propose an optimized selection method for the primary node based on a voting mechanism whereby the consensus-node group and observation-node group vote to produce the primary node, which reduces the probability of malicious nodes acting as the primary node and improves the security of primary node selection. Finally, the identity conversion mechanism between node groups is designed, and the actual behavior of nodes within different groups is given credit rewards or punishment, so as to keep an incentive for nodes to participate in appropriate system behavior and improve the working enthusiasm of nodes. The experimental simulation results show that compared with existing PBFT algorithms, the CG-PBFT algorithm improves the average throughput by 51.3% and reduces the average delay by 64.5%; it greatly improves the operating efficiency of the system and can be more suitable for application in the consortium blockchain scenarios.

Optimizing Blockchain Consensus: Incorporating Trust Value in the Practical Byzantine Fault Tolerance Algorithm with Boneh-Lynn-Shacham Aggregate Signature

The consensus algorithm is the core mechanism of blockchain and is used to ensure data consistency among blockchain nodes. The PBFT consensus algorithm is widely used in alliance chains because it is resistant to Byzantine errors. However, the present PBFT (Practical Byzantine Fault Tolerance) still has issues with master node selection that is random and complicated communication. The IBFT consensus technique, which is enhanced, is proposed in this study and is based on node trust value and BLS (Boneh-Lynn-Shacham) aggregate signature. In IBFT, multi-level indicators are used to calculate the trust value of each node, and some nodes are selected to take part in network consensus as a result of this calculation. The master node is chosen from among them based on which node has the highest trust value, it transforms the BLS signature process into the information interaction process between nodes. Consequently, communication complexity is reduced, and node-to-node information exchange remains secure. The simulation experiment findings demonstrate that the IBFT consensus method enhances transaction throughput rate by 61% and reduces latency by 13% when compared to the PBFT algorithm.

Reaching Consensus in the Byzantine Empire: A Comprehensive Review of BFT Consensus Algorithms

Byzantine fault-tolerant (BFT) consensus algorithms are at the core of providing safety and liveness guarantees for distributed systems that must operate in the presence of arbitrary failures. Recently, numerous new BFT algorithms have been proposed, not least due to the traction blockchain technologies have garnered in the search for consensus solutions that offer high throughput, low latency, and robust system designs. In this article, we conduct a systematic survey of selected and distinguished BFT algorithms that have received extensive attention in academia and industry alike. We perform a qualitative comparison among all algorithms we review considering message and time complexities. Furthermore, we provide a comprehensive, step-by-step description of each surveyed algorithm by decomposing them into constituent subprotocols with intuitive figures to illustrate the message-passing pattern. We also elaborate on the strengths and weaknesses of each algorithm compared to the other state-of-the-art approaches.

BOppCL: Blockchain-Enabled Opportunistic Federated Learning Applied in Intelligent Transportation Systems

In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient countermeasures, incentivized mechanisms, and a secure gradient distribution to combat single-point failure verification attacks. Additionally, we integrate the Byzantine fault-tolerant algorithm (BFT) into the node verification component of the delegated proof of stake (DPoS) to minimize verification delays. We validate our approach through experiments on the MNIST, SVHN, and CIFAR-10 datasets, showing convergence rates and prediction accuracy comparable to traditional OppCL approaches.

A Survey of Blockchain in Governance: Framework Selection and Future Implementation in Indonesian Government

Blockchain technology enables users to connect without the need for a third party or central server. This is achieved through the use of a decentralized system, ensuring that all data and information transacted are encrypted, verified, validated, and stored using mathematical consensus algorithms. This leads to blockchain being recognized as a technology characterized by decentralization, security, anonymity, transparency, immutable data, and trust. Blockchain is frequently associated with digital currency, although digital currency is just one of the outcomes of applying blockchain technology, resulting in cryptocurrencies. Currently, blockchain technology is a trend among academics and practitioners who are researching and developing blockchain technology for application in various domains, including government. Government systems and public servants often encounter issues related to data security. Hence, the research has the purpose to offer comprehension and perspectives on implementing blockchain technology within the government sector to enhance public service information security. The research was carried out by reviewing Scopus-indexed international articles published between 2019 and 2023, which are relevant to frameworks, consensus algorithms, and applications employed in the governmental domain. The research outcomes revealed that the Hyperledger Fabric framework, coupled with the Practical Byzantine Fault Tolerance (PBFT) algorithm, is the most suitable option for potentially developing blockchain-based government or public service applications for future implementation. Regarding this research, there are future challenges in the form of constructing prototypes and evaluating their effectiveness and efficiency. Therefore, further research and development efforts are essential to ensure that the application of blockchain technology in the government sector can be realized as required in the future.

An Optimized Byzantine Fault Tolerance Algorithm for Medical Data Security

Medical data are an intangible asset and an important resource for the entire society. The mining and application of medical data can generate enormous value. Currently, medical data management is mostly centralized and heavily relies on central servers, which are prone to malfunctions or malicious attacks, making it difficult to form a consensus among multiple parties and achieve secure sharing. Blockchain technology offers a solution to enhance medical data security. However, in medical data security sharing schemes based on blockchain, the widely adopted Practical Byzantine Fault-Tolerant (PBFT) algorithm encounters challenges, including intricate communication, limited scalability, and the inability to dynamically add or remove nodes. These issues make it challenging to address practical requirements effectively. In this paper, we implement an efficient and scalable consensus algorithm based on the PBFT consensus algorithm, referred to as Me-PBFT, which is more suitable for the field of medical data security. First, we design a reputation evaluation model to select more trusted nodes to participate in the system consensus, which is implemented based on a sigmoid function with adjustable difficulty. Second, we implement the division of node roles to construct a dual consensus layer structure. Finally, we design a node dynamic join and exit mechanism on the overall framework of the algorithm. Analysis shows that compared to PBFT and RAFT, ME-PBFT can reduce communication complexity, improve fault tolerance, and have good scalability. It can meet the need for consensus and secure sharing of medical data among multiple parties.

Use of Blockchain technology for the exchange and secure transmission of medical images in the cloud: Systematic Review with Bibliometric Analysis

The research questions focused on emerging challenges, solutions, approaches, encryption methods, architectures, and opportunities related to blockchain technology in medical image sharing, and a total of 17 relevant articles were selected from the Scopus database for analysis. The increasing adoption of cloud computing environments in the medical field has raised concerns about security and privacy risks. Within these concerns, problems were identified regarding secure data storage, since centralized storage systems in the cloud mostly lack security effectiveness. Blockchain technology offers a decentralized and secure infrastructure to address these problems. However, a systematic review is needed to identify the strengths and limitations of this technology in this context, therefore the objective of this review is to provide a critical and updated view of the security aspects of the use of blockchain technology in the exchange of medical images in the cloud. The proposed solutions included practical byzantine fault tolerance algorithms, homomorphic encryption, and the integration of smart contracts, among others. In conclusion, this review provides insight into the application of blockchain technology for the secure sharing of medical images in the cloud and identifies key technologies, challenges, solutions, and opportunities in the field.

Creation a Blockchain-based Product Qualification System for E-Marketplace using Practical Byzantine Fault Tolerance Algorithm

An indicator of e-marketplace effectiveness is to facilitate transparent transactions between sellers and buyers by offering good products and adequate infrastructure to handle system requirements. A good product must be in accordance with the product qualification which consists of the classification of feasibility or not, and the benefits of the product. However, this often has problems including those caused by frequent problematic system conditions, non-transparent product qualifications, undistributed data, human error until data security. The researcher proposes the creation of a product qualification system for e-marketplace using blockchain technology. The product qualification system consists of data verification and validation carried out by stakeholders involved in the decentralized network. Transactions that have been executed and validated will be stored in the distributed ledger. The focus of this research is the implementation of blockchain that is allowed to use the PBFT algorithm on product qualification records in the e-marketplace. The purpose of this research is to create a blockchain-based product qualification system for e-marketplace that is transparent and immutable and the successful performance of the system in information storage and management security. The results show the creation of a web-based apps product qualification system that implements a blockchain using Hyperledger Fabric and the performance obtained through system testing and data validation testing.

Grouped Multilayer Practical Byzantine Fault Tolerance Algorithm: A Practical Byzantine Fault Tolerance Consensus Algorithm Optimized for Digital Asset Trading Scenarios.

Based on the practical Byzantine fault tolerance algorithm (PBFT), a grouped multilayer PBFT consensus algorithm (GM-PBFT) is proposed to be applied to digital asset transactions in view of the problems with excessive communication complexity and low consensus efficiency found in the current consensus mechanism for digital asset transactions. Firstly, the transaction nodes are grouped by type, and each group can handle different types of consensus requests at the same time, which improves the consensus efficiency as well as the accuracy of digital asset transactions. Second, the group develops techniques like validation, auditing, and re-election to enhance Byzantine fault tolerance by thwarting malicious node attacks. This supervisory mechanism is implemented through the Raft consensus algorithm. Finally, the consensus is stratified for the nodes in the group, and the consensus nodes in the upper layer recursively send consensus requests to the lower layer until the consensus request reaches the end layer to ensure the consistency of the block ledger in the group. Based on the results of the experiment, the approach may significantly outperform the PBFT consensus algorithm when it comes to accuracy, efficiency, and preserving the security and reliability of transactions in large-scale network node digital transaction situations.

Improvement of Hierarchical Byzantine Fault Tolerance Algorithm in RAFT Consensus Algorithm Election

Currently, blockchain technology is not only gaining momentum in the financial industry but also showing promising developments in other sectors. Due to its suitability for various societal needs, an increasing number of non-financial institutions are expanding the scope of blockchain services beyond finance to areas such as healthcare service management and enterprise governance. The core of a blockchain system lies in consensus, which plays a crucial role in its performance and security. The PBFT algorithm is a widely used fault-tolerant consensus algorithm. However, it suffers from issues like high consensus latency, low throughput, and poor performance. In recent years, the HBFT algorithm has addressed these concerns. This paper proposes an improved HBFT blockchain consensus algorithm tailored for large enterprises and non-financial institutions with multiple independent functions. The goal is to achieve more efficient data management and election strategies that are both more secure than PBFT and more efficient than HBFT. Leveraging the enhanced HBFT consensus algorithm ensures stability and efficiency in the consensus and verification processes between organizations. Additionally, it enables the efficient management of data and value creation in the application context.