Data Integrity Research Articles

Cumulus: Blockchain-Enabled Privacy Preserving Data Audit in Cloud

Data owners upload large files to cloud storage servers, but malicious servers may potentially tamper data. To check integrity of remote data, Proof of Retrievability (PoR) schemes were introduced. Existing PoR protocols assume that data owners and third-party auditors are honest and audit only the potentially malicious cloud server to check integrity of stored data. In this paper we consider a system where any party may attempt to cheat others and consider collusion cases. We design a protocol, Cumulus, that is secure under such adversarial assumptions and use blockchain smart contracts to act as mediator in case of dispute and payment settlement. We use state channels to reduce blockchain interactions in order to build a practical audit solution. The security of the protocol has been proven in Universal Composability (UC) framework. Finally, we illustrate several applications of our basic protocol and evaluate practicality of our approach via a prototype implementation for fairly selling large files over the Ethereum platform. We evaluate the prototype and show that our scheme has comparable performance.

Development of Mathematical Algorithm for Detecting XSS Attacks on Web Applications

The widespread usage of web applications has led to an increase in security threats, with Cross-Site Scripting (XSS) attacks being one of the most prevalent and damaging. Detecting and mitigating XSS attacks is crucial to ensure the integrity and confidentiality of sensitive user data. This article presents the mathematical algorithm and a way to identify XSS attacks using a bounded function from below, which depends on the input string, and highlights its potential impact in bolstering web application security. To construct this function, we used special characters and keywords that are frequently found in the construction of XSS attacks.

Can neotectonic faults influence soil air radon levels in the Upper Rhine Graben? An exploratory machine learning assessment

Radon (Rn) is a naturally occurring radioactive gas that poses a significant lung cancer risk. Subsurface fault zones can act as pathways for fluid and gas migration, potentially amplifying Rn accumulation. This study investigates the impact of fault zones on Rn concentrations within a 25 km2 area in the Northern Upper Rhine Graben, Germany — a region with available detailed geophysical exploration data and active neotectonic faulting. We conducted 597 Rn soil air measurements along precisely located fault zones, integrating a comprehensive range of environmental parameters. Utilizing the advanced machine learning model eXtreme Gradient Boosting (XGBoost) in conjunction with SHapley Additive exPlanations (SHAP) values, we dissected the influence of soil types, environmental factors, and proximity to fault zones on soil air Rn concentrations at a 1-meter depth. Our results reveal that clay-rich soils and cumulative 30-day precipitation are the primary drivers of elevated Rn levels. Proximity to fault zones also significantly influences Rn concentrations, though its impact is less pronounced than the factors mentioned above. Additionally, environmental factors such as wind speed, air pressure, and temperature exhibited lesser effects on Rn levels. The negligible influence of measuring devices and operating personnel increases confidence in data integrity in extensive environmental studies. This study demonstrates the effectiveness of integrating XGBoost with SHAP values to identify and quantify key factors influencing Rn concentrations. By providing a robust framework for enhancing Rn prediction models through machine learning, our findings contribute to improved risk assessments and mitigation strategies, thereby advancing public health and environmental management.

Enabling blockchain-IoT convergence for industrial revolution 4.0 innovation: A critical review

The Fourth Industrial Revolution (4IR) has revolutionised global interactions through the Internet of Things (IoT), connecting over 40 billion devices by 2030, raising significant security, protection, and privacy concerns. Blockchain technology offers a decentralised, secure, and transparent solution for data sharing and storage, leveraging its core characteristics—immutability, data integrity, transparency, and consensus—to enable tamper-proof records, real-time tracking, and secure data sharing in supply chain management and healthcare. Despite high implementation costs, blockchain-IoT convergence promises transformative benefits, including enhanced efficiency, reduced costs, and improved security. This study provides an in-depth analysis of blockchain's applications in IoT, highlighting its potential to enhance data security and privacy, improve supply chain management, facilitate secure healthcare data exchange, and enable real-time tracking and monitoring. Future research directions include scalability solutions, interoperability protocols, regulatory frameworks, blockchain-based supply chain management, and quantum-resistant blockchain networks.

Enhancing Security in Low-power Wide-area (LPWA) IoT Environments: The Role of HSM, Tamper-proof Technology, and Quantum Cryptography

Low-power wide-area (LPWA) networks are integral to expanding Internet of Things (IoT) applications, offering extensive coverage with low power consumption. However, these networks face significant security challenges due to their widespread deployment and inherent constraints. In order to provide secure services in an LPWA IoT environment, important information stored in IoT devices (encryption keys, device unique numbers, etc.) must be safely protected from external hacking or theft by physical access, and it is necessary to develop tamper-proof technology to enhance physical security. Meanwhile, with so many ruggedized IoT devices processing and transmitting sensitive information, security systems are essential to protect the integrity and privacy of IoT data. This paper explores the critical role of hardware security modules (HSMs), tamper-proof technology, and quantum cryptography in enhancing the physical, network, and data security of LPWA IoT environments. We propose operational strategies for HSMs, tamper-proof technology in ruggedized LPWA IoT settings, and a quantum key distribution (QKD)-based IPsec solution for robust network and data security.

Performance enhancement in blockchain based IoT data sharing using lightweight consensus algorithm

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

A Review on Traceability System for Fruit and Vegetables Agricultural Products Using Blockchain

This paper presents a blockchain-based traceability system designed to enhance the transparency and efficiency of the agricultural supply chain for fruits and vegetables. By leveraging blockchain technology, the system enables real-time tracking of products from farm to consumer, ensuring data integrity and reducing the risk of food fraud. Each transaction is recorded on a decentralized ledger, allowing stakeholders—farmers, distributors, retailers, and consumers—to access accurate information regarding product origins, handling processes, and quality metrics. This traceability system not only fosters consumer trust but also assists in compliance with regulatory standards and enhances food safety by enabling quick identification of contamination sources. The implementation of this solution can significantly improve supply chain management, reduce losses, and promote sustainable agricultural practices, ultimately benefiting both producers and consumers

Legal Protection of Notaries in Document Validation through Technology-Based Systems: A Comparative Legal Review of Indonesia, the United States, the Netherlands, and Australia

This paper examines the legal framework and challenges notaries face in adopting technology-based systems for document validation in Indonesia, with a comparative analysis involving the United States, the Netherlands, and Australia. Indonesia's legal framework, particularly Law No. 2 of 2014 on the Office of Notary, has yet to fully accommodate electronic signatures and remote notarization. The requirement for in-person meetings for authenticating notarial acts creates a regulatory barrier to implementing cyber notary services. Furthermore, Indonesian notaries face limited access to essential data systems, such as Dukcapil (Civil Registration Directorate) and BPN (National Land Agency), complicating the verification of personal identity and property documents. In contrast, the United States has adopted e-notary systems, using blockchain technology to ensure data integrity and mitigate fraud risks. The Netherlands provides an integrated platform, allowing notaries to validate documents remotely by accessing national databases in real time. Meanwhile, Australia has implemented a Digital Identity Program to facilitate efficient online authentication of documents. These innovations demonstrate how interoperability, regulatory flexibility, and digital literacy can enhance notary services and legal certainty. This paper proposes regulatory reforms in Indonesia, including revising existing laws to support remote notarization and electronic signatures. Second, developing integrated data platforms for real-time access. Third, providing training programs for notaries to adopt new technologies and ensure data security. By aligning Indonesia’s legal framework with international best practices, cyber notary services can enhance efficiency, security, and legal protection for both notaries and the public, meeting the demands of the digital era.Comparison

“Design of a Trustworthy Cloud-Native National Digital Health Information Infrastructure for Secure Data Management and Use”

Abstract Since 2022, Malawi Ministry of Health (MoH) designated the development of a National Digital Health Information System (NDHIS) as one of the most important pillars of its national health strategy. This system is built upon a distributed computing infrastructure employing the following state-of-art technologies: (1) digital healthcare devices to capture medical data; (2) Kubernetes-based Cloud-Native Computing architecture to simplify system management and service deployment; (3) Zero-Trust Secure Communication to protect confidentiality, integrity and access rights of medical data transported over the Internet; (4) Trusted Computing to allow medical data to be processed by certified software without compromising data privacy and sovereignty. Trustworthiness, including reliability, security, privacy and business integrity, of this system was ensured by a peer-to-peer network of trusted medical information guards deployed as the gatekeepers of every computing node on this system. This NDHIS shall facilitate Malawi to attain universal health coverage by 2030 through its scalability and operation efficiency. It shall improve medical data quality and security by adopting a paperless approach. It will also enable MoH to offer data rental services to healthcare researchers and AI model developers around the world. This project is spearheaded by the Digital Health Division (DHD) under MoH. The trustworthy computing infrastructure was designed by a taskforce assembled by the DHD in collaboration with Luke International in Norway, and a consortium of hardware and software solution providers in Taiwan. A prototype that can connect community clinics with a district hospital has been tested at the Taiwan Pingtung Christian Hospital.

NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking

The paper presents a novel framework for implementing decentralized algorithms based on non-fungible tokens (NFTs) for digital twin management in aviation, with a focus on component lifecycle tracking. The proposed approach uses NFTs to create unique, immutable digital representations of physical aviation components capturing real-time records of a component’s entire lifecycle, from manufacture to retirement. This paper outlines detailed workflows for key processes, including part tracking, maintenance records, certification and compliance, supply chain management, flight logs, ownership and leasing, technical documentation, and quality assurance. This paper introduces a class of algorithms designed to manage the complex relationships between physical components, their digital twins, and associated NFTs. A unified model is presented to demonstrate how NFTs are created and updated across various stages of a component’s lifecycle, ensuring data integrity, regulatory compliance, and operational efficiency. This paper also discusses the architecture of the proposed system, exploring the relationships between data sources, digital twins, blockchain, NFTs, and other critical components. It further examines the main challenges of the NFT-based approach and outlines future research directions.

DSDOS Cloud: A Decentralized Secure Data Outsourcing System With Hybrid Encryption, Blockchain Smart Contract‐Based Access Control, and Hash Authentication Codes for Cloud Security

ABSTRACTWith the increasing trend of outsourcing data to cloud services, ensuring data security and privacy has become crucial. Typically, data are stored on cloud servers in encrypted form to mitigate risks. However, accessing the encrypted data requires an access key distributed by a third party. If this third party is untrustworthy, it poses a significant security threat to the system. To address this challenge, we propose a Decentralized Secure Data Outsourcing System (DSDOS) that uses blockchain technology to ensure data security and privacy. The DSDOS system comprises three modules: data security and privacy, access control and authorization, and data integrity and availability. The data security and privacy module uses a hybrid encryption scheme that combines Advanced Encryption Standard (AES), partially homomorphic encryption (PHE), and Diffie–Hellman (DH) to ensure secure data storage and access. The access control and authorization module uses a blockchain‐based smart contract system to manage access to the encrypted data. The data integrity and availability module uses hash‐based message authentication code (HMAC) to ensure that the data are not tampered with and is always available. We conducted a security and performance analysis of the DSDOS system and found that it outperforms previous schemes in terms of security and performance. The DSDOS system is a secure and privacy‐preserving data outsourcing system that can be used to mitigate the security risks associated with traditional cloud storage systems.

Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment

Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.

Rethinking Methodology and Data Integrity in Mendelian Randomization: Insights from Air Pollution and MASLD Research

Rethinking Methodology and Data Integrity in Mendelian Randomization: Insights from Air Pollution and MASLD Research

Web-Based Task Organizer for Enhanced Productivity

The Web-Based Task Organizer for Enhanced Productivity (WBTO) mainly aims to improve task management and overall increase in productivity. In our system we offer features such as task prioritization, categorization, due dates and recurrence options which is integrated into a user friendly interface. We use a full stack approach using java for the back end logic, JavaScript for a dynamic user interface and a SQL database for preserving data storage. It provides key features like task creation, categorization, prioritization, scheduling and progress tracking and interactive functionalities and ensuring data integrity and privacy. As this not only improves individual productivity but also further provides group work collaboration allowing for much faster task tracking, reminders and analytics which improves overall user experience.

Artificial Neural Network-Based Data-Driven Parameter Estimation Approach: Applications in PMDC Motors

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems.

A Provably Secure and Lightweight Two-Factor Authentication Protocol for Wireless Sensor Network

Wireless Sensor Networks (WSNs) are rapidly being integrated into various fields, significantly impacting and facilitating many aspects of human life. However, the increasingly prominent security issues associated with WSNs have become a significant challenge. This paper provides an in-depth analysis of the security challenges faced by WSNs in resource constrained and open communication environments. As a key component of the Internet of Things (IoT), a WSN can perceive, collect and transmit physical environmental data in real-time, and is widely used in military, medical, agricultural and other fields. However, the insecurity of communication channels and unauthorized user access pose severe threats to network security and data integrity. To address these challenges, this paper proposes a provably secure two-factor authentication protocol. This protocol utilizes a Chebyshev chaotic map and a two-factor authentication mechanism, which not only enhances security in WSNs but also improves authentication efficiency. The protocol is validated through security proof and performance experiments, demonstrating excellent security, functionality and efficiency. This provides strong support for secure and efficient communication in WSNs across various application scenarios.

Improving Data Security in Cloud Computing through the Implementation of a Hybrid Encryption Algorithm

As cloud computing continues to gain popularity, the security of sensitive data stored in the cloud remains a paramount concern for organizations and individuals alike. Traditional encryption methods, while effective, often struggle to balance security, efficiency, and usability. This paper presents a hybrid encryption algorithm that combines the strengths of symmetric and asymmetric encryption to enhance data security in cloud computing environments. The proposed hybrid approach leverages symmetric encryption for fast and efficient data encryption, while asymmetric encryption is utilized for secure key exchange. By implementing this dual-layer encryption strategy, we can significantly improve data confidentiality and integrity during transmission and storage. Additionally, the algorithm incorporates robust key management practices, ensuring that encryption keys are safeguarded against unauthorized access. Through a series of performance evaluations and security assessments, we demonstrate that our hybrid encryption algorithm not only provides superior security but also maintains acceptable levels of performance, making it suitable for real-time applications. The results indicate that this approach effectively mitigates risks associated with data breaches and unauthorized access, positioning it as a viable solution for enhancing data security in cloud computing. This research contributes to the ongoing discourse on cloud security, offering a practical framework for organizations to protect their sensitive information in increasingly complex digital landscapes.

Edge-Driven Linux Platform for Power Quality Solutions

In the modern landscape of data centers, ensuring optimal power quality and efficiency is paramount. This paper describes designing and implementing an edge-driven Linux platform for power quality solutions for real-time measurement, analysis, and conformity with IEC 61000-4-30, IEEE 519, and EN 50160 standards. It employs an AM6442xx processor and has a web interface with the backing of an efficient embedded web server, a NodeJS, and a Nginx server. It supports industrial communication protocols like Modbus TCP to simplify integration into existing systems. In addition, it involves risk assessment, threat modeling, and enhanced ability of the platform to withstand tests that threaten the confidentiality and integrity of data. Thus, the proposed solution increases the efficiency of data center operations and provides reliable power quality metering that meets current international standards.

The role of electronic data capture systems in clinical trials: Streamlining data integrity and improving compliance with FDA and ICH/GCP guidelines

This study investigates the transformative role of Electronic Data Capture (EDC) systems in clinical trials, emphasizing their impact on data integrity, compliance with regulatory standards, and the efficiency of trial processes. The purpose of the research was to critically analyze how EDC systems align with FDA and ICH/GCP guidelines, addressing the complexities of modern clinical trials. By conducting a detailed literature review, the study explores the benefits, challenges, and technological advancements associated with EDC systems. Key findings highlight the significant advantages of EDC systems in enhancing data accuracy, real-time monitoring, and facilitating regulatory compliance through secure data management and audit trails. The study also identifies several challenges, including high implementation costs, data privacy concerns, and integration issues with other digital trial technologies. Technological advancements, such as artificial intelligence, blockchain, and decentralized trial models, are explored as promising solutions to these limitations, offering new avenues for optimizing clinical trial management. The study concludes that EDC systems have revolutionized clinical trials, streamlining data collection and analysis while ensuring compliance with stringent regulatory requirements. It recommends continued investment in EDC technologies, enhanced training for trial staff, and the integration of cutting-edge innovations to ensure future clinical trials' success.

Innovative approaches to risk management in clinical research: Balancing ethical standards, regulatory compliance, and intellectual property concerns

This study explores innovative approaches to risk management in clinical research, focusing on the intricate balance between ethical standards, regulatory compliance, and intellectual property (IP) concerns. As clinical trials become increasingly complex and globalized, managing risks effectively while maintaining participant safety and upholding legal and ethical principles has become paramount. The study adopts a comprehensive review of emerging technologies, such as artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics, to assess their role in transforming risk management strategies. Key findings reveal that AI and predictive analytics significantly enhance real-time monitoring and the early identification of risks, while adaptive trial designs offer flexibility in managing unforeseen challenges during trials. Blockchain technology improves transparency and data integrity, ensuring secure and verifiable data transactions. However, the intersection of IP protections and equitable access to treatments presents ongoing ethical challenges, particularly in low-income regions. The study concludes that while technological innovations provide robust tools for mitigating risks, a balanced approach to regulatory frameworks and IP protections is essential. The study recommends increased collaboration between regulatory bodies, research institutions, and technology providers to harmonize global regulatory standards, ensuring both innovation and accessibility. Furthermore, it advocates for more flexible IP frameworks that address the tension between protecting innovations and ensuring equitable access to life-saving treatments.