Blockchain Nodes Research Articles

BRFL: A blockchain-based byzantine-robust federated learning model

With the increasing importance of machine learning, the privacy and security of training data have become a concern. Federated learning, which stores data in distributed nodes and shares only model parameters, has gained significant attention for addressing this concern. However, a challenge arises in federated learning due to the byzantine attack problem, where malicious local models can compromise the global model's performance during aggregation. This article proposes the Blockchain-based Byzantine-Robust Federated Learning (BRFL) model, which combines federated learning with blockchain technology. We improve the robustness of federated learning by proposing a new consensus algorithm and aggregation algorithm for blockchain-based federated learning. Meanwhile, we modify the block saving rules of the blockchain to reduce the storage pressure of the nodes. Experimental results on public datasets demonstrate the superior byzantine robustness of our secure aggregation algorithm compared to other baseline aggregation methods, and reduce the storage pressure of the blockchain nodes.

The Adoption of Blockchain Technology on Company’s Internal Control System in Sales and Purchasing Cycle

ABSTRACT This paper explores the use of blockchain technology to enhance internal control systems for fraud prevention within the sales and purchasing cycle. Despite existing internal controls, significant weaknesses persist in these systems, leading to issues such as poor performance, missing orders, underpricing, rebates, false charges, falsification of order information, late delivery, and order/performance shifting. Using the design science research approach (Hevner, March, Park, and Ram 2004), we propose a conceptual framework that allocates the verification, storage, and management of events to each authorized blockchain node, preventing tampering with verified data. By integrating blockchain’s secure, traceable, and decentralized features with smart contracts, oracles, and the Internet of Things (IoT), our framework aims to automate workflows, track goods in real-time, and measure operational performance accurately. This approach enhances internal control systems, reduces audit workloads, and mitigates the risk of business fraud and misconduct. JEL Classifications: M42; M49.

Blockchain-based conditional privacy-preserving authentication scheme using PUF for vehicular ad hoc networks

Vehicular ad hoc networks (VANET) have been the key indispensable module of the future intelligent transportation system. Security and privacy are two essential attributes that protect the safe driving of vehicles. Over the last two decades, numerous conditional privacy-preserving authentication schemes have been presented for the VANET environment. However, existing schemes have various limitations, including security issues, high storage overhead, and frequent interactions. In order to bridge these difficulties, this work combines physically unclonable function and blockchain technology to construct a conditional privacy-preserving authentication scheme for the VANET environment. Specifically, we combine physical unclonable function and dynamic pseudonym techniques to generate unique pseudonym IDs dynamically and private keys using physical unclonable function to enhance privacy protection and resist physical attack. To reduce the number of communication rounds during the verification process, we deployed lightweight blockchain nodes to avoid direct communication between the receiver and the blockchain network. The proposed scheme demonstrates resilience against various potential attacks through comprehensive security analysis and proof. Furthermore, performance metrics indicate that our scheme outperforms similar schemes, making it suitable for resource-constrained VANET.

PLCBlox: Using blockchain-based audit trails to generate secure PLC commands

This research paper discusses security issues with current deployments of Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition Systems (SCADA) in the industry and proposes a solution that enables PLC devices to query a blockchain infrastructure for commands and setpoints. The blockchain assumes a dual role in this context: serving as an immutable audit trail database as well as a trusted source for critical commands and setpoints. In contrast to the conventional paradigm, this novel approach does not require write access at the PLC level, thus minimizes its attack surface and helping to protect against known and zero-day vulnerabilities often used in cyberwarfare, such as in the case of the notorious Stuxnet worm. Applications that enforce the logging of user operations for Good Manufacturing Practices (GMP) or compliance purposes use the blockchain network as an audit trail database for user actions. Any attempt to maliciously circumvent the logging operation would not affect the operation of a critical process. Additionally, a prototype implementation developed as part of this research finds that modern PLC devices are more than capable of interacting with private Ethereum blockchain nodes. The required libraries and user code consume a small percentage of available resources, while the duration of a complete request-response cycle measured around 22msec. The authors anticipate that PLCBlox can be used as a drop-in replacement for applications requiring higher security standards and logging enforcement, such as nuclear power plants or other critical infrastructure.

An Optimized Encryption Storage Scheme for Blockchain Data Based on Cold and Hot Blocks and Threshold Secret Sharing.

In recent years, with the rapid development of blockchain technology, the issues of storage load and data security have attracted increasing attention. Due to the immutable nature of data on the blockchain, where data can only be added and not deleted, there is a significant increase in storage pressure on blockchain nodes. In order to alleviate this burden, this paper proposes a blockchain data storage strategy based on a hot and cold block mechanism. It employs a block heat evaluation algorithm to assess the historical and correlation-based heat indicators of blocks, enabling the identification of frequently accessed block data for storage within the blockchain nodes. Conversely, less frequently accessed or "cold" block data are offloaded to cloud storage systems. This approach effectively reduces the overall storage pressure on blockchain nodes. Furthermore, in applications such as healthcare and government services that utilize blockchain technology, it is essential to encrypt stored data to safeguard personal privacy and enforce access control measures. To address this need, we introduce a blockchain data encryption storage mechanism based on threshold secret sharing. Leveraging threshold secret sharing technology, the encryption key for blockchain data is fragmented into multiple segments and distributed across network nodes. These encrypted key segments are further secured through additional encryption using public keys before being stored. This method serves to significantly increase attackers' costs associated with accessing blockchain data. Additionally, our proposed encryption scheme ensures that each block has an associated encryption key that is stored alongside its corresponding block data. This design effectively mitigates vulnerabilities such as weak password attacks. Experimental results demonstrate that our approach achieves efficient encrypted storage of data while concurrently reducing the storage pressure experienced by blockchain nodes.

Blockchain-Based Caching Architecture for DApp Data Security and Delivery.

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model's effectiveness in providing secure and high-performance DApp solutions.

FEATURES OF THE LEGAL STATUS AND CIRCULATION OF CRYPTOCURRENCIES IN THE REPUBLIC OF KAZAKHSTAN AND A NUMBER OF FOREIGN COUNTRIES

This article is devoted to the study of the legal regulation of the issuance, storage, exchange, purchase and sale of cryptocurrencies. Cryptocurrencies have become widely known in modern society after the advent of the Bitcoin system, which was first reported in Forbes magazine in 2011. Cryptocurrencies operate in a decentralized mode and use blockchain technology, which ensures anonymity and security of transfers without the intervention of a banking organization. Blockchain nodes can be located all over the world, which creates a risk of anonymity for offenders. Cryptocurrencies differ from traditional currencies in that they are not issued by the state and are not subject to state control or the influence of the banking system. The article analyzed the legal status of cryptocurrencies in some countries, including Kazakhstan. The problems o f legislative regulation of cryptocurrencies and the impossibility of solving them at the state level are also considered.The relevance of this article is caused by the presence of some problems of legal regulation of cryptocurrencies, such as the lack of a single international standard for regulating cryptocurrencies, the uncertainty of their taxation, the use of cryptocurrencies for illegal transactions.

Dispatcher: Resource-aware Nakamoto Blockchain via Hierarchical Topology and Adaptive Incentives

Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resources can work out the PoW difficulties, thereby increasing the mining cost of malicious attackers and ensuring the security of blockchain systems. Such an assumption of having abundant resources leads to drawbacks of low throughput and risk of centralization. In this article, we present Dispatcher, a novel distributed consensus protocol that takes resource heterogeneity into account to ensure resource-aware PoW with high efficiency. Dispatcher introduces a hierarchical topology to offer flexible PoW difficulties tailored for different nodes’ resources. In particular, it utilizes the limited resource of each node to jointly maximize the performance by concurrent mining. Moreover, we design an adaptive incentive mechanism to fit the available resource of blockchain nodes to rewards. Our experiments show that Dispatcher enjoys a substantial performance margin over the state-of-the-art. We can achieve a 50% throughput improvement compared with OHIE.

Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on Blockchain

As an emerging learning paradigm, Federated Learning (FL) enables data owners to collaborate training a model while keeps data locally. However, classic FL methods are susceptible to model poisoning attacks and Byzantine failures. Despite several defense methods proposed to mitigate such concerns, it is challenging to balance adverse effects while allowing that each credible node contributes to the learning process. To this end, a Fair and Robust FL method is proposed for defense against model poisoning attack from malicious nodes, namely FRFL. FRFL can learn a high-quality model even if some nodes are malicious. In particular, we first classify each participant into three categories: training node, validation node, and blockchain node. Among these, blockchain nodes replace the central server in classic FL methods while enabling secure aggregation. Then, a fairness-aware role rotation method is proposed to periodically alter the sets of training and validation nodes in order to utilize the valuable information included in local datasets of credible nodes. Finally, a decentralized and adaptive aggregation mechanism cooperating with blockchain nodes is designed to detect and discard malicious nodes and produce a high-quality model. The results show the effectiveness of FRFL in enhancing model performance while defending against malicious nodes.

Blockchain-based 6G task offloading and cooperative computing resource allocation study

In the upcoming era of 6G, the accelerated development of the Internet of Everything and high-speed communication is poised to provide people with an efficient and intelligent life experience. However, the exponential growth in data traffic is expected to pose substantial task processing challenges. Relying solely on the computational resources of individual devices may struggle to meet the demand for low latency. Additionally, the lack of trust between different devices poses a limitation to the development of 6G networks. In response to this issue, this study proposes a blockchain-based 6G task offloading and collaborative computational resource allocation (CERMTOB) algorithm. The proposed first designs a blockchain-based 6G cloud-network-edge collaborative task offloading model. It incorporates a blockchain network on the edge layer to improve trust between terminals and blockchain nodes. Subsequently, the optimization objective is established to minimize the total latency of offloading, computation, and blockchain consensus. The optimal offloading scheme is determined using the wolf fish collaborative search algorithm(WF-CSA) to minimize the total delay. Simulation results show that the WF-CSA algorithm significantly reduces the total delay by up to 42.58% compared to the fish swarm algorithm, wolf pack algorithm and binary particle swarm optimisation algorithm. Furthermore, the introduction of blockchain to the cloud-side-end offloading system improves the communication success rate by a maximum of 14.93% compared to the blockchain-free system.

BBS: A secure and autonomous blockchain-based big-data sharing system

Chain of custody is needed to document the sequence of custody of sensitive big data such as various healthcare data. In this work, we propose a secure and autonomous big data sharing system (BBS) based on a permissioned blockchain. In our system, we refer to the data stored at a blockchain node but outside the ledger for sharing as “off-state”, such as the large amounts of data (called “big data”) that cannot be stored directly in a blockchain ledger in practice. To securely and autonomously share the off-state data, we further design an off-state sharing protocol. In the proposed protocol, a sender registers the big data with BBS for sharing. To acquire the big data, an authenticated and authorized receiver has to propose transactions and interact with BBS in five phases, including data transfer authorization, data transfer, two-time key request, and data decryption. The key advantage of BBS is its ability to defeat dishonest users and autonomously perform the aforementioned five phases. The corresponding transactions are recorded in the ledger, serving as chain of custody evidences that document the data trail. To demonstrate the effectiveness of our proposed system, we have designed and implemented prototypes of BBS for sharing big files over two representative permissioned blockchains, i.e., Hyperledger Fabric and Enterprise Ethereum. Extensive experiments are performed to validate the feasibility and performance of our system.

The implications of decentralization in blockchained federated learning: Evaluating the impact of model staleness and inconsistencies

Blockchain promises to enhance distributed machine learning (ML) approaches such as federated learning (FL) by providing further decentralization, security, immutability, and trust, which are key properties for enabling collaborative intelligence in next-generation applications. Nonetheless, the intrinsic decentralized operation of peer-to-peer (P2P) blockchain nodes leads to an uncharted setting for FL, whereby the concepts of FL round and global model become meaningless, as devices’ synchronization is lost without the figure of a central orchestrating server. In this paper, we study the practical implications of outsourcing the orchestration of FL to a democratic setting such as in a blockchain. In particular, we focus on the effects that model staleness and inconsistencies, endorsed by blockchains’ modus operandi, have on the training procedure held by FL devices asynchronously. Using simulation, we evaluate the blockchained FL operation by applying two different ML models (ranging from low to high complexity) on the well-known MNIST and CIFAR-10 datasets, respectively, and focus on the accuracy and timeliness of the solutions. Our results show the high impact of model inconsistencies on the accuracy of the models (up to a 35% decrease in prediction accuracy), which underscores the importance of properly designing blockchain systems based on the characteristics of the underlying FL application.

A Bitcoin-based Secure Outsourcing Scheme for Optimization Problem in Multimedia Internet of Things

With the development of the Internet of Things (IoT) and cloud computing, various multimedia data such as audio, video, and images have experienced explosive growth, ushering in the era of big data. Large-scale computing tasks in the Multimedia Internet of Things (M-IoT), such as mathematical optimization problems, have begun to be outsourced from IoT devices with limited computing power to cloud servers for execution. However, outsourcing computation brings security concerns, because the behaviors of clouds are invisible to users. The leakage of privacy data in outsourced optimization problems leads to immeasurable losses. The mutual distrust between clouds and users causes that the correctness of the optimal decisions and the fairness of the payment activities are not guaranteed. Blockchain technology has the characteristic of immutability and has become a new security paradigm for eliminating multi-party trust concerns. In this article, we propose a Bitcoin-based secure outsourcing scheme to address the aforementioned security concerns. To prevent confidential data leakage, the proposed scheme designs a computable privacy-preserving method for the outsourced optimization problems. To judge the correctness of the optimal decision and reduce verification costs, the proposed scheme designs a low-cost two-layer verification mechanism based on dual theory and blockchain technology. Blockchain nodes reach a consensus on the problem solutions and trigger an automatic fair payment protocol-based Bitcoin. Security analysis and experimental results demonstrate that our scheme guarantees privacy, fairness, and computational efficiency.

Industrial blockchain threshold signatures in federated learning for unified space-air-ground-sea model training

The space-air-ground-sea three-dimensional (3D) network is a comprehensive communication network system. This 3D network combines extensive coverage of satellite communications, adaptability of unmanned aerial vehicle (UAV) communications, reliability of terrestrial communications, and the necessity for maritime communications. These networks generate enormous amounts of data, and training machine learning (ML) models on this data will have a significant impact on industry. At the same time, the availability of such data poses numerous security threats, which can be overcome using Federated Learning (FL). Decentralized training in FL can provide a universal model from local data generated by 3D networks. However, most existing FL frameworks have a centralized server, which questions credibility, single-point failure, and global confidence. To solve these problems, industrial blockchain technology has received a lot of attention by replacing centralized servers in traditional FL, which offers a promising approach to address key issues like data privacy and security. In a blockchain-based system, digital signatures are a core component for ensuring data integrity and system security, however, private key disclosure can pose significant risks. Security can be enhanced by using threshold signatures which provide a more reliable foundation for FL by storing keys in multiple nodes and requiring multiple nodes to collaborate to generate signatures. In this paper, we propose a Threshold signing scheme for ISO/IEC Digital Signature Standards (TDSS) in an industrial blockchain. The TDSS scheme helps FL to achieve truly distributed decentralization for unified space-air-ground-sea model training. The TDSS scheme exploits the SM-2 digital signature algorithm in the ISO/IEC standard when t out of n nodes in an industrial blockchain interact with each other to calculate the signature. The experimental results and analysis show that the TDSS scheme has provable security and efficient against security attacks, which can be applied to large-scale threshold signing scenarios.

Towards blockchain-enabled decentralized and secure federated learning

The conventional machine learning process typically operates under the premise of centralized data aggregation, where all data is collected at a central location for model training. However, this raises substantial privacy concerns when the data contains private information. In this context, federated learning has emerged as a prominent solution for preserving privacy in machine learning. This innovative paradigm allows multiple data owners, or clients, to collaboratively train a machine learning model while keeping their local data unshared. A federated learning task is typically initiated by companies, often referred to as model owners, who do not possess enough training data and are willing to financially remunerate clients who contribute to the development of the federated learning model. This situation demands a trading platform that enables model owners to effectively select clients, while ensuring robustness against malicious clients who execute poisoning attacks for unfair financial gain. To address these issues, we design a contribution-based exploration-exploitation mechanism implemented as a smart contract. This mechanism cherry-picks clients with high data quality based on the Shapley value, which is calculated based on local models to evaluate the contribution of each client. Unlike other state-of-the-art security mechanisms, the proposed mechanism can adapt to various scenarios with heterogeneous data distribution and various attacks, while mitigating the effect of malicious behaviors without compromising training accuracy. To accelerate the time-consuming calculation of the Shapley value, we design a parallel computing algorithm that partitions blockchain nodes into multiple shards and distributes calculation tasks among them. The algorithm improves efficiency and tolerates potential false calculation results from malicious nodes.

Practical Implementation of a Blockchain-Enabled SDN for Large-Scale Infrastructure Networks

The network function virtualization (NFV) feature lies at the core of modern networking, and it allows on-demand real-time integration of new network functions, which is a great benefit for large-scale infrastructure networks. In contrast to the functional benefits, NFV introduces software complexity and computational overhead through additional abstraction layers. The current article addresses the function validation problem in large-scale infrastructure networks of Internet Service Providers (ISPs) and proposes the utilization of blockchain as a validation technology, as opposed to implementing a custom validation solution. The current work showcases a practical architecture implementation to address the service validation in service provider large-scale networks. The POX-based solution to control software-defined networks (SDN) for NFV is extended to offer additional blockchain capabilities. Thus, a blockchain node is integrated and executed in the POX SDN controller. Transaction experiments are performed between two endpoints located in remote locations on the Internet, and the detailed results are presented to validate the utilization of the blockchain technology used on SDNs’ control plane.

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.

A Blockchain-Based Trustworthy Access Control Scheme for Medical Data Sharing

Blockchain is commonly employed in access control to provide safe medical data exchange because of the characteristics of decentralization, nontamperability, and traceability. Patients share personal health data by granting access rights to users or medical institutions. The major purpose of the existing access control techniques is to identify users who are permitted to access medical data. They hardly ever recognize internal assailants from legitimate entities. Medical data will involve multilayer access within the authorized organizations. Considering the cost of permissions management and the problem of insider malicious node attacks, users hope to implement authorization constraints within the authorized institutions. It can prevent their data from being maliciously disclosed by end-users from different authorized healthcare domains. For the purpose to achieve the fine-grained permissions propagation control of medical data in sharing institutions, a trust-based authorization access control mechanism is suggested in this study. Trust thresholds are assigned to different privileges based on their sensitivity and used to generate zero-knowledge proof to be broadcasted among blockchain nodes. This method evaluates the trust of each user through the dynamic trust calculation model. And meanwhile, smart contract is employed to verify whether the user’s trust can activate some permissions and ensure the privacy of the user’s trust in the process of authorization verification. In addition, the authorization transaction between users and institutions is recorded on the blockchain for patient traceability and accountability. The feasibility and effectiveness of the scheme are demonstrated through comprehensive comparisons and extensive experiments.

Cache Optimization Methods Involving Node and Content Sharding of Blockchain in Internet of Vehicles

Blockchain stands out in addressing the data security requirements of the Internet of Vehicles. However, blockchain has storage pressure that cannot be met by most existing nodes. The emergence of Mobile Edge Computing allows nodes closer to the users to undertake the caching and computation process. Although sharding can alleviate the storage pressure on blockchain nodes, frequent cross-shard communication can affect the overall performance of the blockchain. In this paper, combining the features of traffic flow with strong regional similarity as well as inter-node correlation, we propose two sharding methods based on the current Vehicle–Infrastructure–Clouds three-tier service model. The proposed Content Sharding method can optimize node caching and improve the cache-hitting ratio. The proposed node sharding method can effectively reduce the system service delay by assisting nodes to cache the whole blockchain together across the network.

A low-cost blockchain node deployment algorithm for the internet of things

A low-cost blockchain node deployment algorithm for the internet of things