Blockchain-based Order Management Process in Decentralized Production Networks: A Conceptual Framework

The healthcare industry faces significant challenges in safeguarding data security and integrity, particularly with the rise of digital health systems and the sensitive nature of patient information. Blockchain technology offers a decentralized, immutable, and secure solution to address these issues, allowing healthcare organizations to protect and manage patient data effectively. This paper examines how blockchain can enhance data security and integrity within healthcare by examining its key features, such as immutability, transparency, and decentralized access, which collectively provide a robust framework for secure data management. The potential applications of blockchain in healthcare, including patient consent management, access control through smart contracts, and data-sharing solutions, are analyzed alongside challenges such as regulatory compliance, cost, and interoperability. By integrating blockchain, healthcare systems can achieve improved data security, fostering trust and resilience in patient data management. This paper concludes with case studies and discusses future trends in blockchain implementation within healthcare, underscoring its transformative potential for securing data while addressing privacy and ethical concerns. Keywords: Blockchain, healthcare, data security, data integrity, patient data management, smart contracts.

The integration of blockchain technology in biomedical diagnostics offers a promising solution to the challenges of data security and privacy in infectious disease surveillance. As the digitalization of healthcare systems accelerates, the need to protect sensitive health information becomes increasingly critical. Blockchain, with its decentralized and immutable nature, provides a robust framework for ensuring the integrity and confidentiality of biomedical data. This abstract explores how blockchain technology can be leveraged to enhance data security and privacy in the context of infectious disease surveillance, where rapid and accurate data sharing is essential for effective public health responses. Infectious disease surveillance relies on the collection, analysis, and dissemination of large volumes of data, often shared across multiple institutions and geographical regions. Traditional systems for managing this data are vulnerable to breaches, unauthorized access, and data tampering, which can compromise public health efforts and patient privacy. Blockchain technology addresses these vulnerabilities by enabling secure, transparent, and tamper-proof data exchanges. Each transaction or data entry is recorded in a distributed ledger, accessible only to authorized participants, thus ensuring that the data remains secure and unaltered. Moreover, blockchain’s inherent transparency allows for real-time monitoring and auditing of data flows, which is crucial in the timely detection and response to infectious disease outbreaks. The use of smart contracts within blockchain networks further enhances the automation and efficiency of data management, ensuring that data is only accessed and shared according to predefined rules and conditions. This not only safeguards patient privacy but also builds trust among stakeholders, including patients, healthcare providers, and public health authorities. In conclusion, the integration of blockchain technology in biomedical diagnostics presents a transformative approach to addressing the critical issues of data security and privacy in infectious disease surveillance. By leveraging blockchain's unique features, healthcare systems can ensure that sensitive diagnostic data is protected, thus supporting more effective and secure public health interventions in the fight against infectious diseases. Keywords: Blockchain, Biomedical Diagnostics, Data Security, Privacy, Infectious Disease Surveillance.

Digital data is a new factor of production in the information age. In the context of the rapiddevelopment of the latest technologies, such as big data, artifi cial intelligence, cloud computing andblockchain, as well as the continuous development of the digital economy, digital data plays an important role in the development of enterprises. Data security issues affect the development of a countryand its security, are related to public interests, and are also closely related to the personal rights of its citizens. It is necessary to regulate data protection at the legal level.July 2, 2020 the website of the National People’s Congress published the Data Security Law of the People’s Republic of China (draft), which systematically refl ects the current national point of view on data security and development, noting the improvement of data security to the level of state security. Like the Cybersecurity Law, it is a component of the general concept of national security, belongs to the highest level of law and has the highest legal force. This law is of great strategic importance for building a data protection system in the PRC.As the main law in the fi eld of data security management ,the Data Security Law has made certain breakthroughs in the defi nition of the concept of data. The concept of data in the Data Security Law is not limited to network data, but includes information recorded in electronic form and in other ways; the necessary extraterritorial consequences are provided for in the draft Data Security Law, which is of great importance for the protection of national sovereignty and civil rights of the People’s Republic of China; the relationship between data security protection, data development and use is clarifi ed, and the status of data in the development of the digital economy is clarifi ed through legislation in order to fully demonstrate the economic value of data; the main body responsible for security, supervision and overall coordination has been created, the main responsibilities of various regions and departments have been increased, supervision responsibilities have been redistributed, which is more in line with the needs and current situation in data security supervision; a data classifi cation system has been established that promotes the relevant work of data security surveillance agencies; an appropriate mechanism has been created at the state level in the system of data security risk assessment, reporting, information exchange, monitoring and early warning; a data export control system and a mutual data protection system were created, as well as the construction of a cross-border data fl ow system of the PRC was standardized; data protection obligations in organizations and for individuals performing data operations and performing data protection duties were clarifi ed; adhere to the principles of security and development and provide measures to support and promote data security and development; institutional measures have been taken to ensure the security of government data.The draft law on data security contains a comprehensive concept of information security. The upcoming Law on Data Security will contain recommendations and basic principles for ensuring data security and development. All industries will have to pay attention to an integrated management system and various supporting provisions to ensure data security and development. Gradually, a favorable environment will be created for the effective use of data and safe development, which will signifi cantly contribute to the rapid development of informatization and digitization.The Data Security Law includes all types of information in the fi eld of protection. In the future, the Law on Data Security should pay attention to the Law on Cybersecurity, Law on State Security and Law on the Protection of Personal Information, which are under development, supplement and improve today’s relatively disparate legislation related to data security. The laws and regulations of the PRC on data security will be carefully developed on the basis of these three laws in order to fully implement the development, use of data and industrial development, thereby promoting new provisions in the creation of a legal data security system.

Machine learning based on reinforcement learning for smart grids: Predictive analytics in renewable energy management

INTRODUCTION: With the rapid development of cloud computing and intelligent technology, traditional accounting informatization systems are gradually migrating to cloud computing platforms, promoting financial management's automation and intelligence. OBJECTIVES: However, data security in the cloud computing environment, especially the issue of data integrity, has become one of the core challenges for intelligent accounting informatization systems. Accounting informatization systems rely on the storage and processing of a large amount of financial, tax, and transaction data, and the integrity of these data is directly related to the financial transparency, compliance, and decision-making accuracy of an enterprise. METHODS: Therefore, how to design an efficient and scalable cloud data integrity verification algorithm becomes the key to ensuring the reliability and security of intelligent accounting systems. This paper proposes a cloud data integrity verification algorithm for smart accounting informationization based on a cloud computing environment. The algorithm combines the efficient data processing capability of cloud computing with the automation requirements of smart accounting systems to ensure data integrity during storage, transmission, and processing through various data validation techniques such as digital signatures, hash algorithms, and blockchain-based distributed ledgers. RESULTS: The algorithm design considers the dynamics and scalability of the cloud computing environment. It can realize fast, real-time data validation and integrity detection in a large-scale data environment. Through experimental verification, the algorithm proposed in this paper performs well in processing large-scale accounting data with high verification efficiency and accuracy. CONCLUSION: Compared with traditional validation methods, the algorithm improves the speed of data validation. It enhances the system's ability to prevent data tampering and loss, providing a new cloud data integrity assurance scheme for intelligent accounting informatization systems.

Data outsourcing through cloud storage enables the users to share on-demand resources with cost effective IT services but several security issues arise like confidentiality, integrity and authentication. Each of them plays an important role in the successful achievement of the other. In cloud computing data integrity assurance is one of the major challenges because the user has no control over the security mechanism to protect the data. Data integrity insures that data received are the same as data stored. It is a result of data security but data integrity refers to validity and accuracy of data rather than protect the data. Data security refers to protection of data against unauthorized access, modification or corruption and it is necessary to ensure data integrity. This paper proposed a new approach using Matrix Dialing Method in block level to enhance the performance of both data integrity and data security without using Third Party Auditor (TPA). In this approach, the data are partitioned into number of blocks and each block converted into a square matrix. Determinant factor of each matrix is generated dynamically to ensure data integrity. This model also implements a combination of AES algorithm and SHA-1 algorithm for digital signature generation. Data coloring on digital signature is applied to ensure data security with better performance. The performance analysis using cloud simulator shows that the proposed scheme is highly efficient and secure as it overcomes the limitations of previous approaches of data security using encryption and decryption algorithms and data integrity assurance using TPA due to server computation time and accuracy.

Blockchain technology, initially developed for cryptocurrencies, has evolved into a versatile tool with applications across industries, including healthcare, finance, and now sports. This study investigates the role of blockchain in enhancing athlete performance tracking by integrating physical and cognitive metrics. Traditional methods often lack objectivity and security, while blockchain offers decentralised, tamper-proof, and real-time data tracking, addressing these challenges. The primary goal of this article is to explore how blockchain technology can enhance the tracking of athletes' mental and physical performance. The article aims to provide a comprehensive framework for understanding the integration of blockchain in sports, focusing on its potential to address current challenges in performance monitoring, improve data security and integrity, and enable more effective and personalized training and competition strategies. A systematic review of literature was conducted to examine blockchain’s applications in sports. The analysis focused on its capabilities for performance tracking, data security, and integration with wearable technology. Metrics like strength, endurance, attention, decision-making, and cognitive fatigue were evaluated through blockchain-enabled systems. Blockchain enhances both physical and cognitive performance tracking by providing secure, real-time, and tamper-proof data. It facilitates personalised training programs by integrating metrics such as agility, focus, and decision-making. Wearable technology combined with blockchain enables continuous monitoring while safeguarding athlete privacy. The technology also ensures transparent record-keeping, useful for talent scouting and dispute resolution. Blockchain technology revolutionises athlete performance management by addressing traditional monitoring challenges. It offers a holistic approach to integrating physical and cognitive metrics, ensuring data security and enabling predictive insights for training and competition. However, issues like scalability, adoption resistance, and regulatory barriers need resolution for widespread implementation. Future studies should focus on the long-term impacts of blockchain on athlete development, scalability in sports organisations, and integration with emerging technologies like AI and IoT. Addressing regulatory and ethical challenges will be crucial for its adoption in the global sports industry.

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.

Health information systems face major challenges in terms of data security and integrity. This article proposes a blockchain-based framework that enables secure data sharing among various entities in a health information system. The framework ensures the confidentiality, integrity, and transparency of patient data through the use of smart contract technology and hash-based encryption. Case studies on several hospitals demonstrate improved data security without sacrificing system efficiency.

SummaryCloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

Introduction. Protecting outer perimeter is not enough to ensure secure data communication in the information system of local area network. Analytical reports of leading information security companies confirm this fact. Usually, an attacker having overcome the outer perimeter conducts network reconnaissance before carrying out an attack. The success of a network attack depends on the completeness of the information collected. The constantly changing network topology does not provide an attacker with long-term network topology information, as a result, the attacker is forced to collect information more intensively thereby identifying himself. Otherwise, the effectiveness of the planned attack is reduced. The aim of this research is to increase the intra-network data transfer security level by means of network topology dynamic reconfiguration. The authors proposed a new solution for ensuring secure node interaction countering both internal and external attackers having overcome an outer perimeter.Materials and methods. The proposed solution is based on a software-defined network and VxLAN technology. The solution involves constant network reconfiguration both with a certain frequency and on the occurrence of certain events, so that an attacker could not have long-term information. If an intruder is detected or an information security incident occurs, the network is automatically reconfigured in such a way as to lessen or prevent possible consequences.Results. The obtained results show that periodic network changes do not allow an attacker to covertly collect complete information about the network, and the proposed solution may allow to detect and isolate the attacker.Discussion and conclusion. The obtained results show that it is possible to apply the proposed solution for organizing secure data communication within the local computer network of the information system.

Due to the characteristics of resource-constrained in wireless sensor networks (WSNs), energy consumption is a major concern. Data aggregation is an important method to reduce the energy consumption and communication overhead ; however, it suffers from the security problems of data integrity. In fact, designing integrity schemes for data aggregation is an imperative problem for securing WSNs. In this paper, we propose a secure data aggregation based on homomorphic MAC scheme that provide efficient secure data integrity with different trade-offs between security assumptions, computation cost, and communication payload. The scheme uses two types of homomorphic MAC (i.e., MACs and EMACs). Each MAC provides integrity of the transmitted message and each EMAC provides integrity of the MACs. Our experiments show that our scheme requires less computation overheads and energy consumption than previously methods and can effectively preserve data integrity.

Industrial Internet of Water Things architecture for data standarization based on blockchain and digital twin technology☆

In the era of fintech services computing, the convergence of advanced technologies has become pivotal for driving innovation. The seamless integration of IIoT-enabled data streams with artificial intelligence (AI) systems presents unprecedented opportunities for revolutionizing financial risk assessment. Efficient AI-to-AI business platforms are crucial for sustainable and transparent fintech credit risk analysis. However, current practices rely on single AI models, leading to inefficiencies and regulatory challenges. This paper proposes a novel Blockchain Regulatory Framework to address these issues, particularly in lending to small and micro enterprises. The key novelty of our work, compared to existing state-of-the-art, is fourfold: 1) a multi-level guarantee strategy that combines blockchain with AI-AI models to enable distributed risk-sharing among guarantee agencies; 2) a dual-chain blockchain structure for secure and efficient storage of credit data and processes; 3) the integration of Intel SGX with Oblivious RAM (ORAM) to ensure unprecedented confidentiality, integrity, and availability in credit data sharing; and 4) the incorporation of game theory to mathematically validate the anti-collusion properties of the AI-AI decision mechanism. Our findings indicate that the proposed framework offers a comprehensive solution for achieving safe, efficient, and transparent credit assessments. This approach holds significant promise for developing effective financing solutions while ensuring data security, regulatory compliance, and robust credit evaluation methodologies.