Blockchain Network Research Articles

A novel secure privacy-preserving data sharing model with deep-based key generation on the blockchain network in the cloud

Cloud computing is currently emerging as a developing technology in which a Cloud Service Provider (CSP) is a third-party organization that provides effective storage of data and facilities to a large client base. Saving information in a cloud offers users the satisfaction of accessing it without the need for direct knowledge of the distribution and management of an infrastructure. The primary objective is to develop a novel, secure, and privacy-preserving data-sharing model that utilizes deep-based key generation on blockchain in the cloud. Data communication is done using multiple entities. The research aims to develop a collaborative data-sharing method in the cloud for the authentication scheme for cloud security on blockchain and smart contracts. Initialization, registration, key generation, authentication of data sharing, and validation are carried out here. The proposed data-sharing model involves a revenue distribution model that depends on Multiple Services (MS) models to improve multiple cloud services. The security parameters namely passwords, hashing functions, key interpolation, and encryption are used for preserving the Data privacy and here the SpinalNet is used for generating keys. Furthermore, the devised SpinalNet_Genkey obtained a value of 45.001 MB, 0.002, and 0.003 sec for memory usage, revenue, and computation cost.

A blockchain-based architecture for tracking and remunerating fast frequency response

The increasing penetration of renewable sources introduces new challenges for power systems’ stability, especially for isolated systems characterized by low inertia and powered through a single diesel power plant, such as it happens in small islands. For this reason, research projects, such as the BLORIN project, have focused on the provision of energy services involving electric vehicles owners residential users to mitigate possible issues on the power system due to unpredictable generation from renewable sources. The residential users were part of a blockchain-based platform, which also the Distributors/Aggregators were accessing. This paper describes the integrated framework that was set up to verify the feasibility and effectiveness of some of the methodologies developed in the BLORIN project for fast frequency response in isolated systems characterized by low rotational inertia. The validation of the proposed methodologies for fast frequency response using Vehicle-to-Grid or Demand Response programs was indeed carried out by emulating the dynamic behavior of different power resources in a Power Hardware-in-the-Loop environment using the equipment installed at the LabZERO laboratory of Politecnico di Bari, Italy. The laboratory, hosting a physical microgrid as well as Power Hardware-in-the-Loop facilities, was integrated within the BLORIN blockchain platform. The tests were conducted by assuming renewable generation development scenarios (mainly photovoltaic) and simulating the system under the worst-case scenarios caused by reduced rotational inertia. The experiments allowed to fully simulate users’ interaction with the energy system and blockchain network reproducing realistic conditions of tracking and remuneration of users’ services. The results obtained show the effectiveness of the BLORIN platform for the provision, tracking and remuneration of grid services by electric vehicles and end users, and the benefits that are achieved in terms of reducing the number of diesel generating units that need to be powered on just to provide operational reserve due to the penetration of renewable sources, resulting in fuel savings and reduced emissions.

Designing A Blockchain-Empowered Telehealth Artifact for Decentralized Identity Management and Trustworthy Communication: Interdisciplinary Approach.

Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks. The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance. We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype implementation. We used the design science research methodology to implement our overall design. We innovated over existing telehealth platforms with blockchain integration that improves health care delivery services in terms of security, privacy, and efficiency. We adopted a user-centric design approach and started with user requirement collection, followed by system functionality development. Overall system implementation facilitates user requirements, thus promoting user behavior for the adoption of the telehealth platform with decentralized identity management and an access control mechanism. Our investigation identified key challenges to identity management and trustworthy communication processes in telehealth platforms used in the current health care domain. By adopting distributed ledger technology, we proposed a decentralized telehealth platform to support identity management and a trustworthy communication process. Our design and prototype implementation using a smart contract-driven telehealth platform to provide decentralized identity management and trustworthy communication with token-based access control addressed several security challenges. This was substantiated by testing with 10,000 simulated transactions across 5 peers in the Rahasak blockchain network. The proposed design provides resistance to common attacks while maintaining a linear time overhead, demonstrating improved security and efficiency in telehealth services. We evaluated the performance in terms of transaction throughput, smart contract execution time, and block generation time. To create a block with 10,000 transactions, it takes 8 seconds on average, which is an acceptable overhead for blockchain-based applications. We identified technical limitations in current telehealth platforms. We presented several design innovations using blockchain to prototype a system. We also presented the implementation details of a unique distributed architecture for a trustworthy communication system. We illustrated how this design can overcome privacy, security, and scalability limitations. Moreover, we illustrated how improving these factors sets the stage for improving and standardizing the application and for the wide adoption of blockchain-enabled telehealth platforms.

Pioneering Emission Trading in Africa: An Architectural Design of a Blockchain-Powered Carbon Trading System

To achieve carbon neutrality by 2050 and reduce greenhouse gas (GHG) emissions to a range of 350–420 megatons of carbon dioxide equivalent, South Africa, the largest emitter of GHG in Africa, introduced a carbon taxation system in June 2019. However, within just 3 years, it has become evident that the system faces significant challenges, with less than 6% of the estimated tax returns reaching the government. This issue raises concerns about potential corruption and manipulation within the system. To address these challenges, this study presents a comprehensive framework for a carbon trading and taxation system leveraging blockchain technology (BCT). Designing this framework from the government’s perspective ensures efficient monitoring and oversight. Notably, the proposed system operates automatically, eliminating the need for third-party intermediaries. This study also identifies a crucial research gap and lays the foundation for future studies. It empirically implements the system as a decentralised application) using the Hedera and Solana blockchain networks. Complemented by JavaScript for the user interface. Python, through Django, was used to handle the server-side system implementation. Thus, this research aims to address the shortcomings of Africa’s current carbon taxation system through a novel, government-centric approach powered by BCT. The proposed system’s potential to enhance transparency and efficiency justifies further exploration in future studies.

Design And Implementation of Blockchain-Based Security Solutions for Electronic Health Records (EHRS): Enhancing Data Integrity and Privacy

A block chain-based electronic health record (EHR) system is the primary focus of this study. To improve data security and interoperability, healthcare is increasingly looking to cloud-based electronic health record (EHR) systems and block chain technology. It delves into the topic of choosing the right cloud infrastructure, data management methodologies, and block chain technology to guarantee the availability, integrity, and security of patient data. We go over every detail of the system's design and implementation, from the block chain network and cloud storage layer to the user interface. By comparing it to more conventional EHR systems, the pilot research finds that the new system is more capable of protecting, exchanging, and managing patient data. We take a look at the pros, cons, and obstacles that might prevent a cloud EHR system based on block chain from being widely used. Insights and suggestions from this study address the obstacles, give direction for effective adoption, and are of great value to healthcare organisations thinking about implementing such systems. Keywords: Block chain, Security, Solutions, Electronic Health Records (Ehrs) And Privacy

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.

FBLearn: Decentralized Platform for Federated Learning on Blockchain

In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision making, exhibit common features that render them complementary. At the same time, machine learning has become a robust and influential technology, adopted by many companies to address non-trivial technical problems. This adoption is fueled by the vast amounts of data generated and utilized in daily operations. An intriguing intersection of blockchain and AI occurs in the realm of federated learning, a distributed approach allowing multiple parties to collaboratively train a shared model without centralizing data. This paper presents a decentralized platform FBLearn for the implementation of federated learning in blockchain, which enables us to harness the benefits of federated learning without the necessity of exchanging sensitive customer or product data, thereby fostering trustless collaboration. As the decentralized blockchain network is introduced in the distributed model training to replace the centralized server, global model aggregation approaches have to be utilized. This paper investigates several techniques for model aggregation based on the local model average and ensemble using either local or globally distributed validation data for model evaluation. The suggested aggregation approaches are experimentally evaluated based on two use cases of the FBLearn platform: credit risk scoring using a random forest classifier and credit card fraud detection using a logistic regression. The experimental results confirm that the suggested adaptive weight calculation and ensemble techniques based on the quality of local training data enhance the robustness of the global model. The performance evaluation metrics and ROC curves prove that the aggregation strategies successfully isolate the influence of the low-quality models on the final model. The proposed system’s ability to outperform models created with separate datasets underscores its potential to enhance collaborative efforts and to improve the accuracy of the final global model compared to each of the local models. Integrating blockchain and federated learning presents a forward-looking approach to data collaboration while addressing privacy concerns.

ECDSA-secured blockchain architecture for interoperable healthcare in the Internet of Robotic Things (IoRT)

ABSTRACT The ever-growing Internet of Robotic Things (IoRT) in healthcare settings introduces new challenges in securing sensitive patient data. Existing communication networks within IoRT environments are vulnerable to security breaches. This paper proposes an ECDSA-secured blockchain architecture specifically designed to address these challenges. A key innovation is the implementation of a private blockchain network on ZedBoard development boards for access control and data integrity verification. IoT devices collect patient health data, which is then securely transmitted to a Mobius server for storage, minimizing resource impact on ZedBoards. To further strengthen the security framework, the architecture integrates the Elliptic Curve Digital Signature Algorithm (ECDSA) with Radix- 2 w optimizations. These optimizations result in a 28% reduction in CPU usage and an 8.96% reduction in memory usage, significantly enhancing cryptographic performance. Practical implementation validates the effectiveness of the proposed approach, providing a robust solution for securing IoRT networks. This research offers valuable insights for industries reliant on the seamless integration of IoT and robotics and concludes with key findings and future research directions, including exploring energy-efficient cryptographic protocols, enhancing scalability through advanced consensus mechanisms, and developing user-friendly interfaces for managing IoRT networks.

Bodyless block propagation: TPS fully scalable blockchain with pre-validation

Despite numerous prior attempts to boost transaction per second (TPS) of blockchain system, most of them were at a price of degraded decentralization and security. In this paper, we propose a bodyless block propagation (BBP) scheme for which the blockbody is not validated and transmitted during the block propagation process, to increase TPS without compromising security. Rather, the nodes in the blockchain network anticipate the transactions and their ordering in the next upcoming block so that these transactions can be pre-executed and pre-validated before the birth of the block. It is critical, however, that all nodes have a consensus on the transaction content of the next block.This paper puts forth a transaction selection, ordering, and synchronization algorithm to drive the nodes to reach such a consensus. Yet, the Coinbase Address of the miner of the next block cannot be anticipated, and therefore transactions that depend on the Coinbase Address cannot be pre-executed and pre-validated. This paper further puts forth an algorithm to deal with such unresolvable transactions for an overall consistent and TPS-efficient scheme. With our scheme, most transactions do not need to be validated and transmitted during block propagation, ridding the dependence of propagation time on the number of transactions in the block, and making the system fully TPS scalable. Experimental results show that our protocol can reduce propagation time by 4× with respect to the current Ethereum blockchain, and its TPS performance is limited by the node hardware performance rather than block propagation.

A consensus-based solution for cryptocurrencies arbitrage bots in intelligent blockchain

Intelligent blockchain is an emerging field that integrates Artificial Intelligence (AI) techniques with blockchain networks, with a particular emphasis on improving the performance of blockchain, especially in cryptocurrencies exchanges. Meanwhile, arbitrage bots are widely deployed and increasing in intelligent blockchain. These bots exploit the characteristics of cryptocurrencies exchanges to engage in frontrunning, generating substantial profits at the expense of ordinary users. In this paper, we address this issue by proposing a more efficient asynchronous Byzantine ordered consensus protocol, which can be used to prevent arbitrage bots from changing the order of the transactions for profits in intelligent blockchain-based cryptocurrencies. Specifically, we present two signal asynchronous common subset protocols, the more optimal one with only constant time complexity. We implement both our protocol and the optimal existing solution Chronos with Go language in the same environment. The experiment results indicate that our protocols achieve a threefold improvement over Chronos in consensus latency and nearly a tenfold increase in throughput.

Secure cognitive health monitoring using a directed acyclic graph-based and AI-enhanced IoMT framework

Millions of people throughout the world struggle with mental health disorders, but the widespread stigma associated with these issues often prevents them from seeking treatment. We propose a novel strategy that integrates Internet of Medical Things (IoMT), DAG-based hedera technology, and Artificial Intelligence (AI) to overcome these challenges. We also consider the costs of chronic diseases such as Parkinson's and Alzheimer's, which often require 24-hour care. Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration, our system aims to provide low-cost, continuous support. Since IoMT data is large in volume, we need a blockchain network with high transaction throughput without compromising the privacy of patient data. To address this concern, we propose to use Hedera technology to ensure the privacy, and security of personal mental health information, scalability and a faster transaction confirmation rate. Overall, this research paper outlines a holistic approach to mental health monitoring that respects privacy, promotes accessibility, and harnesses the potential of emerging technologies. By combining IoMT, Hedera, and AI, we offer a solution that can help break down the barriers that prevent individuals from seeking the support they need for their mental well-being. Furthermore, comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%, which is more than 30% better than traditional models such as logistic regression.

Artificial neural network-based models for short term forecasting of solar PV power output and battery state of charge of solar electric vehicle charging station

The main objective of this study is to develop ANN-based predictive models for short-term forecasting of solar PV power output and battery state of charge. The 3Ds energy model that integrates Decarbonization, Digitalization, and Decentralization of the energy system to facilitate the shift towards sustainable energy sources is used for this electric vehicle project. The experimental set up includes solar PV panels, a solar inverter, a battery inverter, and a battery bank. Additionally, smart meters were installed to collect real-time performance data from the solar PV-powered electric vehicle (EV) charging station. The weather and real-time system performance data were used to develop short term forecasting models based on Artificial Neural Networks (ANN) and the Levenberg-Marquardt method, to predict the performance of the system (power output and state of the charge) ahead. The R values for the prediction models of solar photovoltaic (PV) specific power and battery state of charge fall within the range of 0.9957–0.9969 and 0.9990 to 0.9996, respectively. Furthermore, the mean squared errors (MSEs) for the artificial neural network (ANN) models pertaining to solar photovoltaic (PV) power output and battery state of charge exhibit a variety of values. Specifically, the MSEs for solar PV power output range from 1.242 x 10^-4 to 1.579 x 10^-4, while the MSEs for battery status of charge range from 0.1889 to 0.3402. Artificial neural network (ANN) models possess considerable promise for practical implementations as they simplify intricate connections among inputs, parameters, and outputs in real-world scenarios. The level of accuracy and short-term predictive models of the solar PV powered EV charging station are very important for achieving a balance between the supply of solar photovoltaic (PV) system and the demand for electric vehicles (EVs). Predictive models will help build complex control mechanisms for controlling and optimizing solar PV-powered charging stations, supervising their operation and maintenance, and simplifying renewable energy pre-purchase. Future works will include the development of an energy management system to improve the efficiency of the solar stations charging the EVs, the establishment of blockchain networks for both the solar PV system and battery bank, and the integration of cybersecurity protocols for the charging station.

Fake Product Detection with Blockchain Technology

Consumers and brands are at serious risk due to the growth of counterfeit goods, especially in regions like Nigeria. Conventional techniques, such border inspections and market raids by the Standards Organization of Nigeria (SON), are inadequate for detecting counterfeit goods. To ensure traceability, transparency, and immutability in the supply chain, this article suggests utilizing blockchain technology. The decentralized and encrypted characteristics of blockchain, when bolstered by smart contracts, enable efficient product tracking from producers to end users, hence impeding the infiltration of fake goods. Using a permissioned blockchain network, this system attempts to confirm the legitimacy of products at every point along the supply chain—manufacturers, distributors, retailers, and end users. The Remix IDE is used to deploy and test Ethereum-based smart contracts that were created in Solidity for the proposed system. This blockchain-based strategy aims to decrease the spread of counterfeit goods, protect consumer confidence, and preserve brand reputation. To offer a user-friendly interface for wider accessibility, future advancements will link this system with decentralized apps (DApps).

Semiotokens, Algorithms, and Blockchain Networks: New Possible Patterns in Legal Thought

AbstractThis paper explores the implications of tokens in the legal discourse when it comes to blockchain networks and the Fourth Industrial Revolution. In doing so, reference is made to the functioning and requirements of blockchain networks opposite to that of Statehood. Methodologically, the argument is built on the semiotic relationship between signifier and signified as outlined in De Saussure (1916) as further developed in the comprehensive work done by Lacan (Écrits (trans. Alan Sheridan), Routledge, 1977). Apparently, the factors that influence this choice are considered to be of a non-legal nature. More properly, current gaps in regulation and the lack of cooperation at international level require resorting to extra-legal approaches. In contrast, the fact that economic and strategic motives play a role, does not in itself make the legal discourse devoid of any meaning. In what follows, I will concentrate on the study of factors that explain why such approach can be fruitful to establish new strategies to adjust the legal discourse to advances in technology and society. I have three reasons for selecting this topic. The first is a more pragmatic reason, based on my current research of law as a linguistic and societal phenomenon. Secondly, the topic does also touch the existing entanglement between law and economy whereby results intersect with the protection of rights inherent to members of blockchain communities. Lastly, this article relies on a dynamic concept of legal validity as a continuous process of constructing, negotiating, and reconstructing the array of blockchain communities’ interactions and transactions. Overall, these suggested methodological approaches aim at providing non-mutually exclusive reading keys to a prospective evolution of legal thought as of the Fourth Industrial Revolution changes. In contrast, they concur to provide new frames of enquiry instead of approaching these topics as objects of analysis from a sole exegetic standpoint. As a matter of fact, textualism lacks the capabilities to achieve consistent and categorial selections along with methodological exclusions that certain variants of its theories claim to accomplish. When understood against the background of a careful delineation of the choices that legal studies require, their purpose helps to give shape to a fundamental question; given the functioning of legal studies to direct choices among otherwise plausible significance of legal meaning, should they make use of an exclusively legal, or a cross-disciplinary approach? This study advocates the latter method. In this context, reference is made to the relational and representational components of property, contracts, and tokens to frame the contextual rather than the strict textual meaning of tokenisation. In this context, the above elements concur to bridge the gap among referents pertaining to separate realms. Differently put, the recourse to these extra-legal categories corresponds to levels of abstraction (Tanenbaum, 1976) functioning. They refer to the structured representation of information, ranging from detailed specifics to broader concepts. Abstraction aids in comprehending complex phenomena by allowing analysis at varying granularities. Employing multiple levels facilitates a systematic understanding, enabling individuals to delve into intricate systems, discern patterns, and derive insights, thereby fostering a comprehensive grasp of multi-layered phenomena.

Computer Allied Intelligence in the Education Resource-Sharing Based inContract Deep Learning

A resource-sharing platform is an online space where individuals or organizations can exchange or access various resources, such as knowledge, skills, tools, or assets. These platforms facilitate collaboration and cooperation among users by providing a centralized hub for sharing resources efficiently. In university higher education, a resource-sharing platform serves as a vital tool for students, faculty, and staff to optimize access to a wide array of educational resources. These platforms enable the sharing of academic materials, such as textbooks, lecture notes, and research papers, fostering collaborative learning environments. This paper proposed the proposed method of Distributed Consensus Blockchain Deep Learning (dCBDL) for a resource-sharing deep learning platform in higher education aims to revolutionize collaborative learning and resource utilization. Initially, the dCBDL model uses a blockchain network that serves as the underlying infrastructure for the platform. This blockchain will store transaction records, verify the authenticity of shared resources, and ensure data integrity and immutability. The proposed model uses smart contracts and decentralized storage with a consensus mechanism for data processing in higher education. Finally, the proposed dCBDL model uses the deep learning model for the classification and assessment of student performance in higher education. The proposed model achieves a classification accuracy of 98% which is significantly higher than the conventional techniques such as blockchain architecture and SVM and LSTM classifiers.

Identifying patterns and mechanisms of AI integration in blockchain for e-voting network security

The study focuses on the enhancement of e-voting blockchain network security through the integration of artificial intelligence. The critical problem addressed is the existing limitations in real-time threat detection and anomaly detection within blockchain transactions. These limitations can compromise the integrity and security of blockchain networks, making them vulnerable to attacks and fraudulent activities. The core results of the research include the development and implementation of sophisticated AI algorithms designed to enhance the monitoring of blockchain transactions and the auditing of smart contracts. These AI-driven advancements introduce unique features, such as the capability to detect and respond to security threats and anomalies in real-time. This significantly strengthens and optimizes the security frameworks of blockchain systems in e-voting. These results are explained by the strategic application of machine learning and natural language processing methodologies. By employing these advanced AI techniques, the study has achieved more accurate and efficient threat detection, thereby addressing the security challenges previously mentioned. The practical applications of these findings are extensive and diverse. Enhanced security mechanisms can be utilized in financial transactions, supply chain management, and decentralized applications, providing a robust framework for improved blockchain-based e-voting security. In conclusion, integrating AI into blockchain security mechanisms addresses current limitations in threat detection and offers a scalable and effective solution for future security challenges

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products

In today’s globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products’ biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method’s reliability, security, and efficiency. The experimental results show that this research scheme’s initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry.

DBUP: Dynamic blockchain UTXO processing for storage efficiency optimization

In traditional blockchain systems, the Unsent Transaction Output (UTXO) dataset stores all generated UTXOs, which typically represent digital assets of the users. UTXOs can be used to track the output status of cryptocurrency transactions. However, with the development of blockchain networks, the UTXO dataset continuously increasing in scale. In the UTXO dataset, a large number of low-value UTXOs occupied huge storage space, making the blockchain dataset expanded. This impacts the running performance of the whole blockchain system. To address this issue, this paper proposes the Dynamic Blockchain UTXO Processing strategy (DBUP) for UTXO storage efficiency optimization. This strategy aims to rapidly consume the low-value UTXOs, effectively alleviating UTXO dataset expansion. Furthermore, Through improved simulated annealing (ISA) algorithm and a UTXO dynamic adjustment mechanism in DBUP, the strategy ensures that the transaction fee in the transaction process remain at a low level. The experimental evaluation indicates that our strategy can achieve the better performance against the Low Value First (LVF) and the First-In-First-Out (FIFO) strategies, enhancing the storage efficiency of blockchain systems.

Deep Feed-Forward Neural Network-Based Biometric Authentication System with Biometric Identity and Reputation Score in Blockchain

The biometric authentication in today’s digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346.

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.