Data Integrity Research Articles

Mathematical model enhancing flash memory reliability through DFT-driven error correction coding

Flash memory, ubiquitous in diverse electronic devices, confronts persistent challenges stemming from inherent errors that jeopardize data integrity. This research situates itself at the intersection of these challenges and advancements, proposing an inventive error correction coding framework that harnesses the unique capabilities of analysis with a hybrid error control coding (HECC) approach. In the proposed work, a mathematical model aimed at enhancing the flash memory by identifying the error pattern within the pages using the discrete fourier transform (DFT). By incorporating distinctive DFT mathematical properties, the proposed technique intends to improve flash memory error correction beyond traditional methods. The flash storage defect detection and rectification results with hybrid error correction coding achieved bit error rate (BER) of 4.3e-6, latency 14.1, mean 15.1 and standard deviation 1.0. Error correction efficiency 98% and storage overhead 10%. With this approach results are significantly improving the error correction efficiency, reduce storage overhead and enhanced adaptability to diverse error patterns.

Intelligent System for Secure Tamper Protocol Model with IoT BlockchainArchitecture for Data Mitigation

In an era characterized by the proliferation of Internet of Things (IoT) devices and the critical importance of data integrity, the need for robust security mechanisms has never been greater. This paper introduces a novel "Secure Tamper Protocol Model" (STPM) integrated with an IoT-based blockchain architecture, designed to address the growing challenges of data mitigation in IoT ecosystems. This research explores the application of Symmetric Homomorphic Hidden Markov Models (SHHMMs) in the context of anomaly detection, with a focus on %G - IoT environments. SHHMMs have shown remarkable promise in accurately identifying anomalies within diverse datasets. The study presents numerical findings indicating the model's high accuracy, precision, recall, and F1-Score, with an initial accuracy of 98.2% reaching 99.0% at Epoch 100. Comparative analysis against traditional methods like Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) models consistently highlights SHHMM's superior performance, demonstrated through Packet Delivery Ratio (PDR), Packet Loss, End-to-End Delay, and Overhead metrics. The integration of blockchain technology further enhances the practicality of SHHMM in ensuring data integrity and security. This research contributes to the advancement of anomaly detection techniques in 5G- IoT applications, offering a blend of precision and robustness.

An Improved Laplace Satellite Tracking Method Based on the Kalman Filter

When photoelectric measuring equipment is used to track satellites, the extraction of the short-term or long-term target often fails because the target is weak, clouds block the target, and/or the sun’s angle is too small, resulting in the loss of the tracking target. In this study, an improved Laplacian satellite tracking method based on the Kalman filter is proposed. Firstly, the improved Laplacian algorithm was used for the initial fitting of the equation of motion of a small amount of measurement data. Judgment of the validity and Kalman filtering was carried out on the current frame’s measurement data to calculate the optimal estimate of the current frame’s orbit data, and the accurate equation of motion was iteratively fitted to obtain high-precision data for predicting the satellite’s orbit frame by frame. Numerical tracking of the equipment was carried out. This method was experimentally validated on an actual optical measurement device. The test results showed that this method can make up for the frequent loss of short-term targets. Under the condition that the maximum deviation is less than 3”, the length of extrapolated data can be up to 30 s and the length of the measurement data was less than 30 s. This method may improve the stability of tracking equipment as well as the accuracy and integrity of the measurement data.

Blockchain Technology Communication Technology Model for the IoT

In the rapidly evolving landscape of the Internet of Things (IoT), effective communication and security are paramount. Blockchain technology offers a transformative solution by providing a decentralized, transparent, and immutable ledger for managing and securing IoT interactions. By leveraging blockchain, IoT systems can enhance data integrity, improve trustworthiness, and streamline communication processes. This paper investigates the integration of blockchain technology within the SYMHIOT framework, focusing on the performance evaluation of various scenarios using a Hidden Markov Model (HMM) to manage IoT networks. The study analyzes key metrics such as transaction success rate, blockchain latency, energy consumption, packet delivery ratio (PDR), and throughput across multiple scenarios and time intervals. The results demonstrate that State 1 consistently yields optimal performance, with an average transaction success rate of 94.0%, blockchain latency as low as 115 ms, and energy consumption of 0.42 J. In contrast, State 3 exhibited the most challenging conditions, with a transaction success rate dropping to 85.3%, latency increasing to 140 ms, and energy consumption rising to 0.52 J. The highest packet delivery ratio of 99.0% and throughput of 260 kbps were also observed in State 1. Scenario 4, representing an optimized system configuration, achieved the best overall performance with minimal network delay (9.7 ms) and the lowest blockchain overhead (12.9%). These findings underscore the potential of leveraging blockchain in IoT environments, offering enhanced security, reduced latency, and improved resource efficiency, making it a robust solution for dynamic and resourceconstrained IoT networks.

Developing Scalable APIs for Data Synchronization in Salesforce Environments

In the modern business landscape, Salesforce has become an essential platform for managing customer relationships, driving sales, and fostering business growth. However, as organizations grow and their data volumes increase, the need for efficient and scalable data synchronization across various systems becomes paramount. Developing scalable APIs for data synchronization in Salesforce environments presents a significant challenge due to the complexity of integrating multiple data sources, ensuring real-time updates, and maintaining data integrity across diverse systems. This paper explores the design and implementation of scalable APIs specifically tailored for data synchronization within Salesforce environments. It delves into the architectural considerations necessary for creating robust, high-performance APIs capable of handling large volumes of data while ensuring consistency and reliability. The discussion begins with an overview of Salesforce’s ecosystem, highlighting the importance of data synchronization in maintaining seamless operations across various departments and systems. A critical component of this study is the examination of different API design patterns that support scalability, such as RESTful services, GraphQL, and event-driven architectures. Each approach is analyzed for its suitability in handling the unique challenges of Salesforce data synchronization, such as managing API limits, optimizing data transfer, and ensuring that data synchronization processes do not disrupt ongoing business activities. The paper also explores the role of middleware solutions, like Enterprise Service Bus (ESB) and API gateways, in facilitating seamless data flow between Salesforce and other systems.

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

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

Blockchain-Based Vendor Management in IT: Challenges and Solutions

The rapid evolution of the IT sector has led to increasingly complex vendor management systems, necessitating innovative solutions to handle the multifaceted challenges associated with these systems. Traditional vendor management practices often struggle with issues related to transparency, security, inefficiencies in communication, and trustworthiness among vendors. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a compelling solution to these challenges. This research paper explores the application of blockchain technology in IT vendor management, focusing on its potential to address critical challenges and enhance the overall efficiency of vendor-related processes. The paper begins by outlining the inherent challenges in conventional vendor management systems, including difficulties in verifying vendor credentials, managing contracts, ensuring data security, and maintaining a reliable audit trail. These challenges often result in operational inefficiencies, increased costs, and potential risks related to vendor fraud or non-compliance. Blockchain technology, known for its secure, transparent, and decentralized nature, offers a transformative approach to these challenges. By providing a distributed ledger that records all transactions in a secure and immutable manner, blockchain can significantly enhance the transparency and security of vendor management processes. The paper examines how blockchain can be used to automate vendor verification, streamline contract management through smart contracts, and ensure data integrity across the vendor lifecycle.

Advancing network security in fintech: Implementing IPSEC VPN and cisco firepower in financial systems

This review paper explores the critical importance of network security in the financial technology (fintech) sector, focusing on implementing IPSEC VPN and Cisco Firepower. IPSEC VPN provides secure communication through encryption, authentication, and data integrity, which is essential for protecting sensitive financial data. Cisco Firepower enhances network security with advanced threat detection and prevention capabilities. The paper discusses the challenges in deploying these technologies, including configuration complexity and integration with existing systems. Future trends in fintech security are also examined, highlighting the potential of AI-driven security measures, zero-trust architectures, and blockchain technology to address evolving cyber threats. Financial institutions can ensure robust security and operational resilience by adopting these strategies.

A novel lightweight multi-factor authentication scheme for MQTT-based IoT applications

The present authentication solutions employed in the Internet of Things (IoT) are either inadequate or computationally intensive, given the resource-constrained nature of IoT devices. This challenges the researchers to devise efficient solutions to embed an important security tenet like authentication. In IoT, the most popular machine-to-machine communication protocol used at the application layer is Message Queuing Telemetry Transport (MQTT). However, the MQTT protocol inherently lacks security-related functions, like authentication, authorization, confidentiality, access control, and data integrity, which is unacceptable for IoT-driven mission-critical applications when connected over public networks. In such a situation, the security is hardened by employing a transport layer security protocol like TLS, which entails significant computational overheads. This paper presents a novel scheme to enhance MQTT security by providing a lightweight multi-factor authentication scheme based on Elliptical curve cryptography. The proposed scheme uses a low-cost signature and a fuzzy extractor to correct errors in imprinted biometrics in noisy environments. This scheme attains mutual authentication, generates a securely agreed-upon session key for secret communication, and guarantees perfect forward secrecy. Furthermore, the rigorous informal security analysis shows the proposed scheme resists cryptographic attacks, including known session critical attacks. Furthermore, an empirical study has been carried out to assess the effectiveness of the proposed scheme in the Cooja simulated environment.

Certificateless integrity auditing scheme for sensitive information protection in cloud storage

Data integrity auditing provides a method for checking the integrity of outsourced data in cloud storage. However, outsourced data often contain sensitive information (such as names), posing risks of exposure during data sharing. To address this issue, Ming et al. proposed a certificateless integrity auditing scheme for sensitive information protection, claiming its security. However, by demonstrating two specific attack scenarios, we pointed out its security vulnerabilities. Subsequently, we proposed a new certificateless integrity auditing scheme for sensitive information protection in cloud storage (CIAS-SIP), which supports sensitive information protection and does not specify the data blocks that need sanitization by the data owner (DO). In addition, it supports dynamic operations by the DO on outsourced data (insertion, deletion, and modification) and provides security proofs based on the discrete logarithm problem. Finally, we compared CIAS-SIP’s performance with three other integrity auditing schemes for sensitive information protection. The results show that CIAS-SIP exhibits superior efficiency.

Advanced Encryption Techniques in Healthcare IoT: Securing Patient Data in Connected Medical Devices

As a result of the fast development of Internet of Things (IoT) technology, the healthcare sector has undergone a transformation. This transformation has been brought about by the deployment of linked medical devices that provide immediate monitoring and data collecting. Despite the fact that these innovations promise to bring about considerable gains in patient care and operational efficiency, they also bring about major security issues, especially with regard to the protection of personally identifiable information about patients. Within the context of the Internet of Things (IoT) ecosystem for healthcare, this study investigates sophisticated encryption approaches that are aimed to protect patient data stored in linked medical equipment. One of the first things that the research does is investigate the specific security needs and risks that are linked with Internet of Things (IoT) devices in the healthcare industry. These include concerns around data privacy, integrity, and authentication. Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), and Quantum Key Distribution (QKD) are some of the encryption standards and protocols that are often used in the process of protecting Internet of Things (IoT) connections. This article presents a complete examination of these encryption standards and protocols. Following that, it digs into sophisticated encryption approaches that are especially targeted to the limits and needs of healthcare IoT systems. These techniques include lightweight cryptographic algorithms, key management strategies, and secure multi-party computing (MPC) frameworks.

Blockchain-Enhanced Traceability Framework for Smart Farming with Integrated Ontology-Based Data Standardization

This paper introduces a new conceptual framework to enhance crop traceability within smart farming environments. The framework merges blockchain technology with ontology-based data standardization to address persistent challenges in traditional traceability systems, such as data fragmentation and inconsistencies. The primary goal is establishing a transparent, reliable, and efficient traceability process from farm to table. The methodology employed in this research follows the Design Science Research Methodology (DSRM), involving iterative cycles of designing, building, and evaluating the proposed framework to ensure its effectiveness in real-world applications. The framework offers a promising solution to improve food safety, quality, regulatory compliance, and data-driven agricultural decision-making by capitalizing on blockchain's immutable and decentralized attributes and leveraging standardized data formats facilitated by ontologies. The proposed framework comprises three key components: ontology development, system integration, and blockchain implementation. Ontology development addresses data fragmentation and inconsistencies by providing a common language and structure for data exchange. Meanwhile, system integration ensures seamless data exchange among stakeholders, and blockchain implementation secures and verifies traceability records to maintain data integrity and trust. This integrated system is expected to foster trust and collaboration among all participants in the agricultural supply chain, thereby advancing the efficiency and sustainability of smart farming practices. Through continuous refinement and validation, this research aims to significantly contribute to the practical implementation of advanced traceability solutions in the agricultural sector, ensuring that these systems are robust, scalable, and adaptable to various farming contexts.

Retraction Note: Prediction of availability and integrity of cloud data using soft computing technique

Retraction Note: Prediction of availability and integrity of cloud data using soft computing technique

Application of Integrated RSA Encryption in Remote Data Integrity Check

Application of Integrated RSA Encryption in Remote Data Integrity Check

Examine the Obstacles to RME Implementation with the PIECES Method Based on the User's Perspective

Karel Sadsuitubun General Hospital, similar to numerous other healthcare facilities, has adopted RME. Implementing RME is essential for enhancing the effective and efficient management of risks. The approach employed in this study is qualitative description with an emphasis on case studies. Data were gathered via semi-structured, in-depth interviews involving three informants: TPPRJ officers, TPPRI, and reporting officers. Employing purposive sampling, the interviews explored six aspects, revealing seven key points concerning obstacles encountered in implementing RME at Karel Sadsuitubun Hospital. These barriers include RME system performance, system speed, features in the RME system, accuracy of system information, data integrity, technical constraints, resources and financing systems, and data security in the system. The implementation of the RME system in this hospital still faces various problems such as system performance, system speed, data integrity, technical constraints, resources and financing systems, and data security.

Prediction of Hard Drive Failure using Machine Learning

The reliability of hard drives is paramount for maintaining data integrity and availability in cloud services and enterprise-level data centers where unexpected failures significantly impact operational efficiency and general performance. This work aims to develop a predictive model using regression analysis to accurately forecast imminent hard drive failures based on historical operational data, specifically SMART (Self-Monitoring Analysis and Reporting Technology) attributes. The study evaluated various regression models which comprises Decision Tree, Random Forest, Support Vector Machine (SVM), Gradient Boosting, and Neural Network. The outcomes indicated that the Random Forest model, with an MSE of 24.7427 and an R2 of 0.9876 and the Neural Network model, with an MSE of 22.6011 and an R2of 0.7442, as the best performing models as they demonstrated high predictive accuracy and robustness. In contrast, the SVM model showed poor performance with an MSE of 2888.8623 and a negative R2of -0.4465. Based on these outcomes, the Random Forest and Neural Network models are recommended for predicting hard drive failures as they delivered a balance of accuracy and interpretability.

DECENTRALIZED VS. CENTRALIZED DATABASE SOLUTIONS IN BLOCKCHAIN: ADVANTAGES, CHALLENGES, AND USE CASES

his study provides a comprehensive examination of decentralized and centralized database solutions within the realm of blockchain technology, exploring the inherent strengths and weaknesses of each approach and their implications for various industries. Decentralized databases, characterized by distributed ledger technology, offer robust security and transparency by eliminating the need for a central authority, thereby reducing the risk of data tampering and fostering trust among participants. However, these benefits come with significant trade-offs, particularly in terms of scalability and performance, as the consensus mechanisms required in decentralized systems often result in slower transaction processing and higher resource consumption. In contrast, centralized databases, typically used in private or permissioned blockchains, excel in operational efficiency and scalability, enabling faster transaction processing and more streamlined management due to the presence of a central authority. Nevertheless, centralized systems introduce vulnerabilities, such as single points of failure and reduced transparency, which can undermine trust and compromise data integrity. The study also highlights industry-specific preferences, with sectors like healthcare and public governance leaning towards decentralized solutions for their emphasis on data integrity, while industries such as finance and logistics favor centralized systems for their superior performance and low latency. Moreover, the research identifies a critical gap in the literature regarding hybrid database models that could potentially integrate the strengths of both decentralized and centralized systems, offering a balanced approach that mitigates their respective weaknesses. These findings underscore the necessity for a tailored approach to database selection in blockchain implementations, aligned with the specific needs and goals of different industries, and suggest that further exploration of hybrid models could lead to more effective and adaptable blockchain solutions in the future.

Unrecognizable yet identifiable: Image distortion with preserved embeddings

Biometric authentication systems play a crucial role in modern security systems. However, maintaining the balance of privacy and integrity of stored biometrics derivative data while achieving high recognition accuracy is often challenging. Addressing this issue, we introduce an innovative image transformation technique that effectively renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models, which allows the distorted photo version to be stored for further verification. While initially intended for biometrics systems, the proposed methodology can be used in various artificial intelligence applications to distort the visual data and keep the derived features close. By experimenting with widely used datasets LFW and MNIST, we show that it is possible to build the distortion that changes the image content by more than 70% while maintaining the same recognition accuracy. We compare our method with previously state-of-the-art approaches. We publically release the source code.11https://github.com/ZamDimon/distortion-generator/tree/v1.0.0.

A semantic axiomatic design for integrity in IoT

AbstractIn the complex, critical, and rapidly evolving era of Industry 4.0 manufacturing, mature engineering disciplines can be developed to validate system designs at an abstract level, enabling effective fault‐free environments. However, designing integrated IoT platforms presents significant challenges due to their inherent complexity and interoperability issues. To address this challenge, the proposed framework integrates axiomatic design principles with considerations for data integrity and domain ontology. Furthermore, this paper introduces a multi‐level integrity rule repository for IoT application to provides a robust foundation for designing and maintaining IoT systems. To validate the effectiveness of this approach, we conducted a study across four domains of smart cities. The design's quality was assessed using model quality parameters, including completeness, consistency and validity. Our analysis shows that the semantic‐aware axiomatic design framework can effectively address the problem of inconsistencies and low‐quality design in scientific IoT environments, providing a more reliable and efficient solution.

A lossless probabilistic image encryption algorithm based on QSD decomposition and matrix transformations

With the development of Internet technology, images have become widely used in people's daily life. However, some images carry significant importance and sensitivity, making the security and protection of digital information more crucial. Standard encryption schemes such as RSA, DES, and AES, which have proven effective for textual data, face limitations when applied to images due to their unique features, such as large data capacity and strong pixel correlation. In recent years, numerous effective image encryption schemes utilizing diverse techniques have been proposed. However, the use of integer decomposition in encryption techniques is still underexplored. This paper introduces a novel, lossless, and probabilistic image encryption scheme designed to enhance the security of sensitive images during transmission and storage. This scheme is based on a new integer decomposition technique and utilizes a key with a very large key space. In the proposed approach, an image is encrypted into a pair of images using quasi-square decomposition and new matrix transformations. Security is further reinforced through the use of orthogonal matrices, which enhance diffusion while ensuring data integrity and preservation. Experimental results demonstrate that the proposed scheme is highly secure against most known attacks and cryptanalysis methods. Additionally, a comparison with various existing image encryption techniques shows the effectiveness of the proposed approach. This research provides a significant contribution by introducing new techniques that leverage integer decomposition, which are highly effective against CPA (Chosen Plaintext Attack) and KPA (Known-Plaintext Attack). Moreover, these techniques enhance the entropy value of encrypted images and improve the values of NPCR and UACI, which help prevent differential attacks. The proposed scheme has the potential to advance applications in secure image transmission, storage, and privacy protection.