Data trace as the scientific foundation for trusted metrological data: a review for future metrology direction

As healthcare systems increasingly adopt fog computing to improve responsiveness and real-time data processing at the edge, significant security challenges emerge due to the decentralized architecture. The traditional perimeter-based security models are inadequate for addressing the dynamic and distributed nature of fog networks, leaving them vulnerable to unauthorized access, data tampering, and latency issues. Therefore, this paper proposes a novel security framework that integrates blockchain (BC) and software-defined network (SDN) technologies, underpinned by zero-trust (ZT) principles, to address these challenges in latency-sensitive healthcare environments. The proposed framework enhances security by combining BC’s immutable transaction logs for data integrity and traceability with SDN’s dynamic network reconfiguration for real-time access control and anomaly detection. The integration of BC and SDN supports continuous authentication and monitoring using cryptographic protocols (SHA-256A and RSA-2048) to secure data transmission. Additionally, tasks are dynamically allocated to fog nodes based on a multi-metric scheduling mechanism that considers fog node capacity, proximity, and compliance with predefined security protocols. The framework was evaluated using iFogSim, simulating a healthcare environment with 50 IoT devices, 10 fog nodes, and varying workloads (100–1000 tasks/min). The key evaluation performance metrics include intrusion detection rate (IDR), data integrity (DI), task completion rate (TCR), average task response time (ART), and average block time. The implementation results demonstrate satisfactory improvements compared to existing models: a 40% increase in IDR, a 30% enhancement in DI, a 15.29% rise in TCR, and a 39.66% reduction in ART. Moreover, the baseline IDR (85%) and DI (70%) were drawn from ZT-1, while TCR (85%) and ART (300 ms) were measured using ZT-2 as benchmarks. These findings illustrate the feasibility of integrating BC, SDN, and ZT principles to mitigate threats such as unauthorized access, data tampering, and delays in latency-sensitive tasks.

In the digital era, cloud computing has become integral to modern data storage and processing, offering scalability and cost-effectiveness. However, ensuring data integrity—defined as the accuracy, consistency, and reliability of data—remains a critical challenge. Breaches or corruption can lead to severe operational, financial, and reputational damage. This research explores the application of Artificial Intelligence (AI) to strengthen data integrity in cloud environments. Leveraging machine learning for anomaly detection, deep learning for pattern recognition, and AI-based automation for real-time monitoring, the study proposes a robust framework to address data integrity threats. It examines prevalent issues like unauthorized access and data tampering, highlighting the limitations of traditional methods such as cryptography and manual audits. By integrating AI into cloud infrastructure, this research emphasizes a proactive approach to anticipating and mitigating threats. Through case studies and experimental results, the study demonstrates the potential of AI-driven solutions to enhance trust and reliability in cloud computing, paving the way for future innovations in this critical domain.

Healthcare data management has undergone significant transformation with the widespread adoption of Electronic Health Records (EHR). However, this evolution also presents critical challenges related to data security, privacy, and interoperability. Traditional EHR systems often fall short in implementing robust safeguards against unauthorized access, data tampering, and breaches, putting sensitive patient information at risk. Addressing these concerns is vital to ensure trust in healthcare systems and compliance with stringent regulatory frameworks. This paper investigates the potential of blockchain technology as a solution to enhance the security and reliability of EHR systems. Blockchain's inherent characteristics, including its immutable and decentralized architecture, align closely with the requirements for improving data integrity, privacy, and accessibility. Key features of blockchain, such as distributed ledgers, cryptographic security, and consensus mechanisms, offer a compelling framework to address vulnerabilities in conventional EHR systems. By conducting a comprehensive literature review, this study identifies recurring issues in existing EHR platforms, such as susceptibility to breaches, unauthorized data manipulation, and the lack of seamless interoperability among stakeholders. To evaluate blockchain's viability, the research developed a prototype solution by integrating blockchain technology with an open-source EHR platform, OpenEMR. Smart contracts were employed to automate data access permissions and enforce data integrity. The prototype underwent rigorous testing in simulated healthcare environments to assess its performance in ensuring data confidentiality, integrity, and availability. The results demonstrate that the proposed blockchain-based system effectively mitigates many of the security and privacy concerns prevalent in traditional EHR systems. Additionally, it enhances transparency and facilitates secure data sharing among authorized stakeholders without compromising patient confidentiality.

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.

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: One Health Approach, Zoonotic Disease, Early Detection, Development, Portable Diagnostic Device.

Secure traceability mechanism of green electricity based on smart contracts and provenance model

With the prevalence of digital cameras, multimedia data have been used to record facts and provide evidence of events. However, the integrity of multimedia data is vulnerable to attacks with the proliferation of data tampering tools. In fact, an effective multimedia content authentication system should support compliant editing (cropping, rotation, compression, and so forth) and have the ability to detect malicious data tampering. Data traceability is a feasible strategy to verify the integrity and provenance of multimedia data. Besides, the privacy of multimedia data needs to be protected to prevent unauthorized access. In this paper, we trace transformations of multimedia data privately by integrating a transparent and immutable blockchain with trusted hardware that provides the capability of private computation. Our system exploits a hybrid storage pattern that separately stores multimedia data off the blockchain and their hashes on the blockchain. With this, our system ensures data integrity and addresses the issue of blockchain's storage capability. Experimental results and analysis show that our solution is efficient and verifiable. A lightweight verifier merely needs to store block headers and is able to validate query results returned by full nodes.

With the extensive application of the Global Navigation Satellite System (GNSS), the intelligent upgrading of the GNSS monitoring system is of particular significance. Traditional GNSS monitoring systems typically rely on a centralized architecture, which possesses certain drawbacks when it comes to data tampering, fault tolerance, and data sharing. This paper presents an intelligently upgraded localized GNSS monitoring system that integrates blockchain and artificial intelligence (AI) technology to achieve the deep integration of security, transparency, and intelligent processing of monitoring data. Firstly, this paper employs blockchain technology to guarantee the integrity and tamper-resistance of GNSS monitoring data and utilizes a distributed ledger structure to realize the decentralization of data storage and transmission, thereby enhancing the anti-attack capability and reliability of the system. Secondly, the LSTM model is utilized to analyze and predict the vast amount of monitoring data in real-time, enabling the intelligent detection of GNSS signal anomalies and deviations and providing real-time early warnings to optimize the monitoring effect. Based on this architecture, we also combine the trained model with smart contracts to realize real-time monitoring and early warnings of GNSS satellites. By integrating the security guarantee of blockchain and the intelligent analysis ability of AI, the localized GNSS monitoring system can offer more efficient and accurate data monitoring and management services. In the study, we constructed a prototype system and tested it in both simulated and real environments. The results indicate that the system can effectively identify and respond to GNSS signal anomalies, and enhance the monitoring accuracy and response speed. Additionally, the application of blockchain enhances the immutability and traceability of data, providing a solid foundation for the long-term storage and auditing of GNSS data. The introduction of AI algorithms, especially the application of the Long Short-Term Memory (LSTM) network in anomaly detection, has significantly enhanced the system’s ability to recognize complex patterns.

ABSTRACT: As cloud computing continues to play a pivotal role in data storage and processing, ensuring the security and integrity of transferred data remains a critical concern. This project introduces a novel approach for secure data transfer and detection leveraging the Counting Bloom Filter in cloud computing environments. The proposed system addresses the vulnerabilities associated with traditional data transfer methods by incorporating the Counting Bloom Filter, a probabilistic data structure. This technology aids in optimizing storage efficiency and enhances the accuracy of data detection, crucial for maintaining data integrity during transmission. The project focuses on these cure transfer of sensitive information within cloud computing environments, providing an added layer of protection against unauthorized access and potential data corruption. The Counting Bloom Filter is employed to detect anomalies and ensure the integrity of the transferred data, contributing to a robust and reliable cloud-based data transfer mechanism. Key objectives of the project include the implementation of advanced encryption techniques for secure data transfer, the integration of the Counting Bloom Filter for efficient data detection, and the development of a comprehensive system ensuring data integrity in the cloud. The project's innovation lies in its ability to enhance the security and reliability of data transfer in cloud computing, addressing the evolving challenges associated with data protection and privacy. In conclusion, the Secure Data Transfer and Detection from Counting Bloom Filter in Cloud Computing project represents a significant advancement in the realm of secure data transmission. By combining encryption methods with the unique capabilities of the Counting Bloom Filter, the project offers an effective solution for securing data during transfer, ensuring the integrity of information in cloud computing environments. Keywords:- Cloud computing, Data storage, Data processing, Data transfer, Security, Integrity, Counting Bloom Filter, Probabilistic, data structure, Storage efficiency, Data detection, Sensitive information, Encryption techniques, Unauthorized access, Data corruption, Anomaly detection, Data protection, Privacy, Secure data transfer, Cloud-based data transfer, System development, Data transmission.

Cyber-physical system (CPS) integration in the field of contemporary healthcare has completely changed patient monitoring and management. But because these systems are linked, they are vulnerable to different kinds of cyber attacks, which calls for strong security protocols. This work aims to proactively identify and reduce possible security risks by proposing a Rule- Based Threat Analysis Methodology (RBTAM) specifically designed for CPS in healthcare environments. RBTAM uses formalized rules to methodically evaluate threats in the physical and cyberspaces. Its foundations are in discrete mathematics. The process starts with identifying the parts and vulnerabilities of the system, then rules capturing possible threat scenarios are formulated. Among the many things covered by these regulations are denial of service, hardware malfunctions, data tampering, and unauthorized access. By means of an extensive analysis process, RBTAM assesses the probability and consequences of threats found on the availability, confidentiality, and integrity of healthcare systems. Through the facilitation of the prioritization of mitigation measures, this analysis helps healthcare professionals to efficiently distribute resources and reduce possible hazards to data security and patient safety. Important elements of RBTAM are the creation of threat scenarios specific to healthcare settings, the development of security rules based on system architecture, and the incorporation of real-time monitoring systems to identify and address new threats. Furthermore, the approach stresses feedback loops for ongoing improvement, which guarantees flexibility to changing cyberthreats and technology developments. We provide a case study of RBTAM application in a simulated CPS healthcare setting to illustrate its effectiveness. Results show that by efficiently identifying and reducing possible security risks, RBTAM improves the general resistance of healthcare systems to cyberattacks. In the ever-connected healthcare environment, RBTAM provides healthcare professionals and system administrators the tools and insights they need to protect patient well-being and data integrity through a proactive and methodical approach to threat analysis in CPS healthcare.

The Internet of Vehicles (IoV) has emerged as a transformative technology, enabling seamless communication among vehicles and infrastructure to improve road safety, traffic efficiency, and passenger comfort. However, the pervasive collection and exchange of data in IoV environments raise significant privacy concerns, as sensitive information about vehicle locations, driving patterns, and personal preferences may be exposed to unauthorized parties. To address these challenges, this study proposes a novel approach that integrates homomorphic encryption with blockchain to ensure privacy-preserving communication in IoV networks. IoV networks rely on the continuous exchange of data among vehicles, roadside units, and centralized servers to support various applications, including traffic management, navigation, and emergency services. However, the centralized nature of traditional communication architectures poses inherent privacy risks, as sensitive data may be vulnerable to interception, tampering, or unauthorized access. Data integrity was ensured through blockchain storage, with an observed tamper-proof rate of 99.9%, effectively preventing unauthorized access or manipulation of exchanged messages. Despite the additional computational overhead introduced by homomorphic encryption and blockchain operations, our system maintained efficient communication capabilities, achieving an average latency of 50 milliseconds and a throughput of 1000 messages per second. Moreover, scalability was demonstrated as our framework seamlessly accommodated an increasing number of vehicles and communication nodes, with observed linear scalability up to 100,000 connected vehicles. Security analyses revealed robust protection against eavesdropping, data tampering, and replay attacks, with a detection rate exceeding 98%. Overall, our results underscore the viability and effectiveness of our integrated approach in providing privacy-preserving communication for IoV networks, paving the way for secure and resilient connected transportation systems. As IoV continues to evolve, our approach can contribute to the development of privacy-enhancing technologies that empower users to fully leverage the benefits of connected transportation while safeguarding their privacy rights.

The rapid digitalization of healthcare systems has led to an unprecedented accumulation of sensitive patient data across various platforms, exposing the industry to growing risks of data breaches, unauthorized access, and integrity compromise. Ensuring the security, privacy, and trustworthiness of healthcare data is paramount to maintaining patient confidentiality and enhancing clinical decision-making. This study proposes a conceptual framework that synergistically integrates Blockchain technology and Artificial Intelligence (AI) to enhance healthcare data security. The framework is designed to address key challenges such as data integrity, access control, real-time threat detection, and secure interoperability across healthcare stakeholders. Blockchain, with its decentralized and immutable ledger capabilities, provides a robust foundation for tamper-proof data storage and transparent audit trails. Smart contracts are employed to automate access controls and ensure compliance with regulatory requirements. AI, on the other hand, plays a critical role in intelligent threat detection and anomaly monitoring. By leveraging machine learning algorithms, the framework can identify suspicious patterns, detect insider threats, and predict potential breaches in real time. The proposed architecture comprises four core components: Secure data ingestion, blockchain-based data storage, AI-powered analytics, and a privacy-preserving access management layer. The framework also incorporates role-based authentication and homomorphic encryption techniques to enhance data privacy while supporting authorized data sharing. Case scenarios such as electronic health record (EHR) exchange and remote patient monitoring are used to demonstrate the practicality and scalability of the model. This integrated approach not only ensures the confidentiality, integrity, and availability of healthcare data but also fosters trust among patients, providers, and regulatory bodies. The framework aligns with global healthcare data standards and complies with regulations such as HIPAA and GDPR. Future directions include the deployment of federated learning models to further decentralize AI training while maintaining data privacy across institutions.

PurposeA multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body and quality evaluation. This paper aims to discuss the aforementioned idea.Design/methodology/approachFirst, the differential body is set on a rotation platform before measuring. Then one laser sensor called as “primary sensor”, is installed on the intern of the differential body. The spherical surface and four holes on the differential body are sampled by the primary sensor when the rotation platform rotates one revolution. Another sensor called as “secondary sensor”, is installed above to sample the external cylinder surface and the planar surface on the top of the differential body, and the external cylinder surface and the planar surface are high in manufacturing precision, which are used as datum surfaces to compute the errors caused by the motion of the rotation platform. Finally, the sampled points from the primary sensor are compensated to improve the measurement accuracy.FindingsA multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body. Based on the characteristics of the measurement data, a gradient image-based method is proposed to distinguish different objects from laser measurement data. A case study is presented to validate the measurement principle and data processing approach.Research limitations/implicationsThe study investigates the possibility of correction of sensor data by the measurement results of multiple sensors to improving measurement accuracy. The proposed technique enables the error analysis and compensation by the geometric correlation relationship of various features on the measurand.Originality/valueThe proposed error compensation principle by using multiple sensors proved to be useful for the design of new measurement device for special part inspection. The proposed approach to describe the measuring data by image also is proved to be useful to simplify the measurement data processing.

The rapid expansion of the Internet of Things (IoT) has revolutionized how devices collect, exchange, and process data across a wide range of applications. However, the highly distributed and resource-constrained nature of IoT systems presents significant security challenges, particularly in data integrity, device authentication, and network resilience. Traditional centralized security models, while effective in controlled IT environments, often fail to scale securely in IoT ecosystems due to single points of failure and insufficient auditability. This study explores the integration of blockchain technology into IoT networks as a decentralized solution to address these security vulnerabilities. Through a mixed-methods research design involving both simulation-based experiments and statistical analysis, the paper evaluates blockchain’s impact on unauthorized access prevention, tamper resistance, energy efficiency, and latency. A private Ethereum test network was implemented, with smart contracts deployed to automate authentication and access control. Results demonstrate that blockchain significantly reduces unauthorized access attempts and prevents data tampering, with a slight trade-off in latency and energy consumption. A Chi-Square test confirmed the statistical significance of reduced breaches in the blockchain model (χ²(1) = 5.26, p = 0.021). While performance overhead was observed, it remained within acceptable limits for non-critical applications. The findings affirm blockchain's potential as a transformative security layer for IoT environments, especially when data integrity, traceability, and decentralized trust are required. Future research is suggested to enhance scalability, reduce resource demands, and integrate AI for intelligent threat detection in blockchain-enabled IoT systems.

This project focuses on securing data storage and transmission using end-to-end encryption while implementing a data deduplication mechanism. The core objective is to ensure that identical files are not duplicated within the system, thus optimizing storage space and minimizing unnecessary redundancy. In the event that a user uploads a file with the same name as an existing file, the system will flag it as a duplicate and prevent its storage. The project uses ABE technique encryption, one of the most secure encryption methods available, to protect user data. This encryption ensures that the file content remains inaccessible to unauthorized users, even if intercepted during transmission. Users will be able to search for their files using encrypted identifiers, preventing exposure of sensitive information during the search process. A proxy server facilitates secure communication between the user and the storage system. The server serves as an intermediary, forwarding data requests securely and ensuring the integrity of the information being exchanged. However, malicious users pose a threat to this system, as they may attempt to exploit vulnerabilities to launch attacks, such as data tampering or unauthorized access. The system has been designed with robust security measures to mitigate these risks and protect the integrity of the stored data. By combining data deduplication with advanced encryption techniques and secure data transmission protocols, this project aims to provide a highly secure, efficient, and scalable solution for managing sensitive files while protecting against potential malicious attacks