Ensure Data Integrity Research Articles

Secure low-cost photovoltaic monitoring system based on LoRaWAN network and artificial intelligence

The advanced study will focus on developing a secure and cost-effective photovoltaic monitoring system using Long Range Wide Area Network (LoRaWAN) and Artificial Intelligence technologies for highly risky missions. With the world shifting to using renewable energy, the development of secure and efficient cost-effective monitoring systems for photovoltaic installations becomes very important. However, most of the solutions in use for PV installation monitoring are characterized by high costs, hence their limited use, especially in developing regions. In this regard, the present work presents country-specific protocols designed for the country of Turkey. The proposed system in this study is expected to offer real-time monitoring and data collection at a very meager cost by utilizing inexpensive microcontrollers and sensors. The accuracy rate demonstrated by the research is highly above board: 97.65%. It costs $210, with a very low power consumption of 0.5W and a range of 10 km. The major features underpin real-time monitoring with wide area coverage for effectiveness and efficiency in PVs monitoring. Test results in many places around Turkey have turned out reliable and accurate to justify a possible reduction in operational costs without compromising the quality of data received. This design is reinforced by a detailed mathematical model that assures one of its thorough analysis in performance. There is custom-built firmware that enhances communication efficiency and ensures data integrity in implementation. Comparative studies carried out with existing technologies indicate that the proposed system is cost-effective and operationally sound. This work gives a scalable, low-cost solution that probably might raise the uptake of PV systems in economically constrained areas.

A global adoption of cryptocurrency and blockchain technology into the healthcare system

Abstract Background/Objectives Blockchain technology is revolutionizing various industries by enhancing security, transparency, and efficiency. In healthcare, its small-scale application demonstrates the potential to transform healthcare. This study explores how cryptocurrency and blockchain technology enhance healthcare financial stability, secure medical information exchange, increase payment recovery and interoperability. Methods Guided by the Diffusion of Innovations (DOI) Theory, this study reviews 34 articles on blockchain and cryptocurrency adoption in healthcare. We performed a thematic analysis focusing on empirical data, theoretical analyses, and existing implementations. Data were categorized by DOI elements: relative advantage, compatibility, complexity, trialability, observability, and social system. The findings were synthesized into a detailed framework for adopting these technologies in healthcare. Results Our analysis shows that cryptocurrency offers a secure, decentralized payment platform, eliminating the need for third-party intermediaries. Blockchain adoption enhances payment and patient record protection. Cryptographic methods ensure data integrity, transparency, and security, preventing modifications once recorded. Altering data requires consensus from the network majority, protecting healthcare providers from ransomware attacks. Conclusions Evidence suggests that cryptocurrency and blockchain will disrupt healthcare payments, data management, and accommodate interoperability within the next decade. These technologies promise improved security, improved public health research, and increased trust in the healthcare system. Key messages • Our research will be assessing the challenges of integrating digital coins and blockchain technology into the healthcare system. • Examine the potential benefits and risks of implementing blockchain technology in healthcare settings.

Applications of Big Data and Blockchain Technology in Food Testing and Their Exploration on Educational Reform

This study reviews the applications of big data (BD) and blockchain technology in modern food testing and explores their impact on educational reform. The first part highlights the critical role of BD in ensuring food safety across the supply chain, discussing various data collection methods, such as national and international food safety databases, while addressing the challenges related to data storage and real-time information retrieval. Additionally, blockchain technology has been explored for its ability to enhance transparency, traceability, and security in the food-testing process by creating immutable records of testing data, ensuring data integrity, and reducing the risk of tampering or fraud. The second part focuses on the influence of BD and blockchain on educational reform, particularly within food science curricula. BD enables data-driven curriculum design, supporting personalized learning and more effective educational outcomes, while blockchain ensures transparency in course management and credentials. This study advocates integrating these technologies into curriculum reform to enhance both the efficiency and quality of education.

Research on the Impact and Solution Strategies of Blockchain Technology on Data Security and Transparency in Enterprise Digital Transformation

Abstract: This research delves into the profound impact of blockchain technology on enhancing data security and transparency during enterprises digital transformation. By dissecting blockchains core attributesdecentralization, immutability, and advanced securitythis study elucidates how it offers pragmatic solutions to prevalent challenges such as data breaches and opaque information processing. Leveraging an extensive literature review, meticulous case analysis, and expert insights, the article comprehensively illustrates blockchains diverse applications in ensuring data integrity and enhancing transparency across critical sectors, including financial services, supply chain management, and healthcare. Furthermore, the study identifies and deliberates on existing research gaps in blockchain technologys application within digital transformation, proposing a systematic framework to deepen comprehension of its role in this process. Moreover, it conducts an in-depth analysis of the technical, operational, and environmental hurdles hindering blockchain adoption, offering a series of action strategies to enable enterprises to harness their potential effectively amidst the swiftly evolving digital landscape. Through this comprehensive inquiry, the article underscores the strategic significance of blockchain technology in augmenting corporate operational transparency and data security. Additionally, it elucidates how blockchain can bolster corporate governance, compliance, and stakeholder trust. Overall, this synthesis provides a thorough review and profound insights into research findings, underscoring blockchains pivotal role in addressing contemporary digital challenges while furnishing invaluable strategic guidance for industry innovation and advancement. The research outcomes hold substantial implications for practitioners and researchers, signaling the transformative potential of blockchain technology in reshaping enterprise operations and charting a course for future exploration in this pivotal domain.

Public Authentic-Replica Sampling Mechanism in Distributed Storage Environments

With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%.

The Critical Role of SREs in Safeguarding Healthcare Data Integrity and Availability

This article explores the critical role of Site Reliability Engineers (SREs) in the rapidly evolving landscape of healthcare technology. As healthcare organizations increasingly adopt digital solutions, SREs have become indispensable in ensuring data integrity, system availability, and regulatory compliance. The article discusses the unique challenges faced by SREs in healthcare environments, including managing vast amounts of sensitive patient data, implementing robust security measures, and maintaining high system reliability. It examines the key responsibilities of SREs, such as proactive monitoring, incident response, data consistency management, and security compliance. Furthermore, the article highlights the significant impact of SRE practices on healthcare delivery, including enhanced patient care, improved operational efficiency, risk mitigation, and cost reduction. Through a combination of statistical data and industry insights, this work underscores the growing importance of SREs in shaping the future of healthcare technology and improving overall patient outcomes.

The Privacy-Centric Data Pipeline : Strategies for Implementing Robust Security Measures in Data Engineering

This article presents a comprehensive framework for addressing data privacy and security challenges in modern data engineering environments. As organizations grapple with ever-increasing volumes of sensitive data and an evolving regulatory landscape, data engineers face complex challenges in safeguarding information throughout its lifecycle. We explore key components of a robust data protection strategy, including regulatory compliance, technical safeguards, and organizational practices. Our analysis covers critical areas such as encryption methodologies, access control mechanisms, data masking techniques, and lifecycle management policies. We also examine the role of data governance frameworks and the importance of continuous employee training in maintaining a secure data ecosystem. By synthesizing current best practices and emerging trends, this article provides data engineers and organizations with actionable insights for implementing effective privacy and security measures. Our findings underscore the need for a holistic approach that integrates technical solutions with strong governance practices to ensure data integrity, confidentiality, and compliance in an increasingly data-driven world.

Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration

Federated learning offers a framework for developing local models across institutions while safeguarding sensitive data. This paper introduces a novel approach for heart disease prediction using the TabNet model, which combines the strengths of tree-based models and deep neural networks. Our study utilizes the Comprehensive Heart Disease and UCI Heart Disease datasets, leveraging TabNet’s architecture to enhance data handling in federated environments. Horizontal federated learning was implemented using the federated averaging algorithm to securely aggregate model updates across participants. Blockchain technology was integrated to enhance transparency and accountability, with smart contracts automating governance. The experimental results demonstrate that TabNet achieved the highest balanced metrics score of 1.594 after 50 epochs, with an accuracy of 0.822 and an epsilon value of 6.855, effectively balancing privacy and performance. The model also demonstrated strong accuracy with only 10 iterations on aggregated data, highlighting the benefits of multi-source data integration. This work presents a scalable, privacy-preserving solution for heart disease prediction, combining TabNet and blockchain to address key healthcare challenges while ensuring data integrity.

An Efficient Multilayer approach for Securing E-Healthcare Data in Cloud using Crypto – Stego Technique

Healthcare data has been moving to cloud platforms in recent years, which has increased accessibility and scalability but also raised security issues. Ensuring data integrity and safeguarding private health information from unwanted access are critical. This paper presents a comprehensive strategy to integrate effective Elliptic Curve Cryptography ECC-AES with steganography techniques to improve the security of healthcare data in the cloud. ECCAES is especially well-suited for cloud situations with limited resources since it provides strong security with reduced key sizes. Confidentiality is guaranteed by encrypting healthcare data using ECC- AES before storage, reducing the possibility of data breaches. Steganography techniques are also integrated to improve security against skilled adversaries by adding an extra degree of obfuscation by concealing encrypted data inside innocuous files or images. Strict key management procedures, access control systems, and frequent security audits are important components of the proposed system that ensure adherence to Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) compliance requirements pertaining to healthcare data protection. Programs for employee awareness and training are also crucial for reducing the likelihood of human mistakes. Healthcare businesses can safely use cloud technology while protecting patient data integrity and privacy by putting in place multi-layered security safeguards. The proposed system provides the multilayer security on healthcare data in cloud environment than other existing systems.

DATA SECURITY IN IOT DEVICES AND SENSOR NETWORKS FOR ROBUST THREAT DETECTION AND PRIVACY PROTECTION

The rapid proliferation of Internet of Things (IoT) devices and sensor networks has revolutionized various industries by enhancing automation, connectivity, and operational efficiency. However, these advancements have also introduced significant security challenges due to the resource constraints and decentralized nature of IoT environments. This paper provides a systematic review of IoT security solutions, focusing on encryption techniques, authentication protocols, and machine learning-based anomaly detection methods. A total of 55 peer-reviewed articles were analyzed following the PRISMA guidelines. The findings reveal that while lightweight cryptographic algorithms, such as elliptic curve cryptography (ECC), offer robust security with low energy consumption, scalability across large IoT networks remains a challenge. Blockchain-based authentication has emerged as a promising decentralized solution, but issues related to energy consumption and latency hinder its widespread adoption. Machine learning techniques have shown high accuracy in detecting threats in real-time, but their resource-intensive nature limits their application in low-power IoT devices. This review underscores the need for multi-layered, integrated security frameworks and highlights gaps in research on quantum-resistant cryptography and interoperable security standards. Future research must focus on developing scalable, energy-efficient security solutions to ensure data integrity and privacy in expanding IoT ecosystems.

Multi-bit error detection and correction technique using HVDK (Horizontal-Vertical-Diagonal-Knight) parity

In a data stream, errors are quite likely to occur and sometimes this is much more terrible. So, data safety is very important in digital systems, especially in critical and real-time systems, microprocessors, embedded systems, computer memory, and data communication. The probability of soft error increases with the exponential rate of increasing transistor per chip, operational voltage, particle strike, condensation of bit-cell area, etc. To ensure data integrity, safety, and system reliability, error detection, and correction are fundamental components of data transmission and storage systems. Existing error correction techniques can solve several bits of error. However, these existing methods are not fully efficient, as some consume a lot of time, space, and bit overhead. An ideal approach will have the potential to minimize all of these parameters. This research paper proposes a novel error correction approach with horizontal, vertical, diagonal, and knight (HVDK) parity bits. This approach has been taken to correct 5-bit errors in 64 bits of data word using the parity-based technique with less bit overhead. Our research advances the knowledge of error correction methods and sheds light on how to pick and use parity bit schemes that are appropriate for different applications.

Quantum cryptography in the security of cognitive radio networks

Cognitive radio networks face challenges from malicious users who aim to disrupt decision-making during spectrum sensing and decrease spectrum efficiency. The proposed model seeks to distinguish between the presence and absence of primary user signals using energy detection and also it introduces a quantum-inspired outlier detection mechanism for cognitive radio networks to enhance security and reliability during spectrum sensing by ignoring the identified outliers. It employs BB84 quantum key distribution (QKD) protocol and one time pad for secure transmission of secondary user (SU) local decisions to the fusion center (FC), ensuring data integrity and confidentiality. These decisions are transmitted securely establishing a shared secret key between SU and FC and arrives at a global decision. To make the model more robust, we proposed another novel model with a modified BB84 protocol and compared the results of both the models, offering a robust solution for enhancing the reliability and performance of cognitive radio networks with proven results from simulation.

A deep learning approach for non-invasive Alzheimer’s monitoring using microwave radar data

Over 50 million people globally suffer from Alzheimer’s disease (AD), emphasizing the need for efficient, early diagnostic tools. Traditional methods like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans are expensive, bulky, and slow. Microwave-based techniques offer a cost-effective, non-invasive, and portable solution, diverging from conventional neuroimaging practices. This article introduces a deep learning approach for monitoring AD , using realistic numerical brain phantoms to simulate scattered signals via the CST Studio Suite. The obtained data is preprocessed using normalization, standardization, and outlier removal to ensure data integrity. Furthermore, we propose a novel data augmentation technique to enrich the dataset across various AD stages. Our deep learning approach combines Recursive Feature Elimination (RFE) with Principal Component Analysis (PCA) and Autoencoders (AE) for optimal feature selection. Convolution Neural Network (CNN) is combined with Gated Recurrent Unit (GRU), Bidirectional Long Short Term Memory (Bidirectional-LSTM), and Long Short-Term Memory (LSTM) to improve classification performance. The integration of RFE-PCA-AE significantly elevates performance, with the CNN+GRU model achieving an 87% accuracy rate, thus outperforming existing studies.

Algorithm for Key Transparency with Transparent Logs

Background Cryptography plays a crucial role in securing digital communications and data storage. This study evaluates the Transparent Key Management Algorithm utilizing Merkle trees, focusing on its performance and security effectiveness in cryptographic key handling. Methods The research employs simulated experiments to systematically measure and analyze key operational metrics such as insertion and verification times. Synthetic datasets are used to mimic diverse operational conditions, ensuring rigorous evaluation under varying workloads and security threats. Implementation is carried out using R programming, integrating cryptographic functions and Merkle tree structures for integrity verification. Results Performance analysis reveals efficient insertion and verification times under normal conditions, essential for operational workflows. Security evaluations demonstrate the algorithm's robustness against tampering, with approximately 95% of keys verified successfully and effective detection of unauthorized modifications. Simulated attack scenarios underscore its resilience in mitigating security threats. Conclusions The Transparent Key Management Algorithm, enhanced by Merkle trees and cryptographic hashing techniques, proves effective in ensuring data integrity, security, and operational efficiency. Recommendations include continuous monitoring and adaptive algorithms to bolster resilience against evolving cybersecurity challenges, promoting trust and reliability in cryptographic operations.

Multiple levels of crypto-fiduciary security unforgeable stamp

The paper introduces the concept of a multi-level crypto-fiduciary security system, focusing on the development and implementation of an unforgeable stamp mechanism. This security architecture integrates cryptographic techniques with fiduciary principles to create a robust authentication framework that ensures data integrity, confidentiality, and trust in transactions. The unforgeable stamp serves as a cryptographic seal that is tamper-proof, traceable, and resistant to counterfeiting or duplication, providing enhanced protection against fraud and unauthorized access. The proposed system leverages multiple layers of encryption, key management, and blockchain technology to achieve a secure and verifiable method of transaction validation. This work aims to address critical security challenges in financial, governmental, and other sensitive applications, where trust and data authenticity are paramount.

Simulation-Based Evaluation of Advanced Threat Detection and Response in Financial Industry Networks Using Zero Trust and Blockchain Technology

The financial sector is increasingly facing advanced cyber threats, necessitating a shift from traditional security measures to more dynamic frameworks. This study presents a novel integration of Zero Trust architecture with hybrid access control system and blockchain technology to enhance security in financial institutions. Zero Trust enforces continuous authentication and dynamic access controls, while blockchain secures digital identities and transaction logs through its immutable ledger, ensuring data integrity and non-repudiation. The proposed framework, evaluated using OMNeT++ simulations enhanced by Ethereum-Ganache, shows improved detection accuracy, reduced false positives, and increased resistance to insider threats and other attacks. It also strengthens compliance with regulatory requirements through robust audit trails, providing enhanced protection for sensitive financial data.

B-ERAC: Blockchain-Enabled Role-Based Access Control for Secure IoT Device Communication

Security risks are increasingly concerning as the Internet of Things (IoT) expands. Authentication, access control, and authorization present significant challenges for resource-constrained IoT devices. Traditional authentication methods often require enhancements for these devices, but Blockchain technology presents a potential solution. Decentralized and distributed, Blockchain eliminates a single point of failure and relies on Elliptic Curve Cryptography (ECC) for robust security. We have introduced a cutting-edge solution to fortify communication security within IoT devices across supply chain ecosystems. By harnessing the power of Blockchain technology, our framework incorporates smart contracts, adheres to ES256 encryption standards, and seamlessly integrates with Infura API. These components establish stringent access controls, ensure data integrity, and enhance transparency throughout supply chain processes. The framework’s robust architecture facilitates swift and secure transactions, bolsters traceability efforts, and effectively mitigates potential security risks. With its scalable design and reliable functionality, this framework emerges as a pivotal asset for optimizing IoT device communication within dynamic supply chain environments. The use of ProVerif in our analysis provides a formal guarantee of the correctness of our access control mechanisms.

A Distributed Intrusion Detection Framework for Vehicular Ad Hoc Networks via Federated Learning and Blockchain

The emergence of connected vehicles via Vehicular Ad Hoc Networks (VANETs) has revolutionized transportation but has also brought forth challenges in security and privacy due to their open architecture. Early detection of intrusions within VANETs is paramount for ensuring safe communication. This research presents an intelligent distributed approach that leverages federated learning (FL) and blockchain for intrusion detection in VANETs. Through FL, various neural network models were implemented to distribute model training among vehicles, thus preserving privacy. Quantitative evaluation metrics demonstrate the effectiveness of the proposed framework. For example, compared to a traditionally trained Stochastic Gradient Descent (SGD) model, the Federated Trained Model achieved higher precision across various attack types, ranging from 68% to 94%, and consistently outperformed in terms of recall, with rates ranging from 57% to 88%. These results highlight FL's superiority in detecting intrusions, evidenced by gains in accuracy, recall, and precision. Integration of FL with blockchain further strengthened security and privacy protection, ensuring data integrity during collaborative FL training across decentralized nodes. This novel framework addresses VANET vulnerabilities by facilitating privacy-preserving, collaborative anomaly monitoring in a trustworthy manner. Evaluations validate the performance advantages of FL for intrusion identification, supporting wider adoption of vehicular technologies. The study underscores the potential of combining FL and blockchain to enable robust, cooperative abnormality recognition crucial for maintaining reliability, safety, and trust in VANET operations.

Enhancing Smart Healthcare Networks: Integrating Attribute-Based Encryption for Optimization and Anti-Corruption Mechanisms

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 milliseconds, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74%. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

High-fidelity model development of CO2 booster refrigeration systems in supermarkets using field measurements

Supermarket refrigeration systems adopting traditional refrigerants with high global warming potential (GWP) have impacts on global warming for indirect and direct greenhouse gases (GHG) emissions. CO2 is a popular low-GWP alternative. The transcritical operation of CO2 systems worsens their energy performance, but provides recoverable heat as a heat source to reduce gas consumption. To evaluate operation performance, data-driven models, trained by historical data, are weak in implementation with datasets outside the scope of training data; in contrast, theoretical models have better extrapolation ability to calculate all operation conditions of CO2 systems at supermarket. Existing theoretical modeling approaches often lack validation against the limited public-access data, which reduces model reliability for further applications, and adopt oversimplified inference methods for unmeasured variables, which increases the risks of breaking thermodynamic laws and lowering model accuracy. This study therefore develops a steady-state theoretical model for CO2 booster refrigeration systems validated against field measurements from three UK supermarkets. The available measurements are utilized to the best level to ensure model accuracy and physical interpretability. Proposed methods to infer missing variables in CO2 systems include condenser outlet temperature, evaporating temperature, compressor isentropic efficiency and compressor mass flow rate. Results show that proposed inference methods enhance the abilities of the proposed modelling approach to ensure data integrity, avoid breaking thermodynamic laws, and improve model accuracy by reflecting real-time actual values of unmeasured variables rather than rough assumptions. The proposed modeling approach provides satisfactory accuracy validated using high-resolution measurements across the whole year from three real supermarkets.