Data Integrity Research Articles

Some aspects of the development of the internal control system at the enterprise

The Company establishes internal control as a measure to counteract violations and a tool to protect the interests of the company. Internal controls are procedures implemented by a company to prevent fraud, increase accountability, and ensure the integrity of financial data. Internal controls are unique to each company and are designed according to the size and structure of the company. Effective internal control is aimed at achieving the company’s goals and protecting the company’s interests. Internal control not only eliminates risks for the company, but also reduces unnecessary costs or efforts. Internal control ensures that the company complies with federal laws and regulations in the field of financial data management. Reliable internal controls can improve operational efficiency and ensure the reliability of financial statements during internal or external audits.

Generative Architecture for Data Imputation in Secure Blockchain-enabled Spatiotemporal Data Management

In the era of big data, one of the most critical challenges is ensuring secure access, retrieval, and sharing of linked spatiotemporal data. To address this challenge, this paper introduces a groundbreaking blockchain-enabled evolutionary indirect feedback graph algorithm for the secure management of interconnected spatiotemporal datasets. The algorithm utilizes a generative neural network model for data imputation, predicting and generating plausible values to improve dataset completeness and integrity. The core architecture utilizes blockchain technology to optimize data retrieval efficiency and uphold robust access control mechanisms. The algorithm incorporates indirect feedback mechanisms, allowing users to provide implicit feedback through their interactions, enhancing the relevance and efficiency of data retrieval. In addition. sophisticated graph-based techniques are used to model intricate relationships between data entities, facilitating seamless data retrieval and sharing in interwoven datasets. The algorithm’s data security approach includes comprehensive access control mechanisms, encryption, and authentication mechanisms, safeguarding data confidentiality and integrity. Extensive evaluations show significant enhancements in retrieval performance and access control precision, making the proposed model a promising solution for the secure management of expansive interconnected spatiotemporal data.

Techniques to alleviate blockchain bloat: Potentials, challenges, and recommendations

Blockchain (BC) bloat, characterized by excessive data growth on BC networks, presents a pressing challenge. The need for a comprehensive study addressing BC bloat solutions arises due to the scarcity of knowledge in this emerging area. This study advances knowledge along with practical insights and inspires innovative approaches to address BC bloat. This research explores various techniques proposed to combat BC bloat. Two technique themes are identified: on-chain and off-chain techniques. On-chain techniques focus on updating the consensus mechanism, block size, or database partition. This study identified five on-chain techniques: structural modification, pruning, sharding, ephemeral BC, and zk-SNARKs. Off-chain techniques focus on processing in parallel, bundling transactions, or handling transactions off-chain. Further, three off-chain techniques identified are off-chain storage, historical data storage, and light clients’ techniques. These recommendations aim to optimize resources use, enhance scalability, and maintain data integrity. However, these may compromise several BC capabilities, such as security, privacy, and data loss. The applicability of these techniques for resource-constrained devices is also explored. Future research should include further investigation into the performance and applicability of these techniques, especially in resource-constrained environments like IoT and fog computing.

Renewable energy management in smart grid with cloud security analysis using multi agent machine learning model

Machine learning makes it possible for challenging jobs to be finished totally on their own. Computers as well as mobile devices may make it simpler to regulate interior temperature, keep an eye on security, and carry out periodic maintenance in a smart grid (SG). The smart grids' capacity to detect cyber attacks is crucial for determining the availability and dependability of the operation. Integrity of bogus data cyber attacks in physical layers of smart grids (SGs) is highlighted in this article. This study suggests a unique method for managing the energy usage of smart grids while analysing the security of data transfer. Here, the energy efficiency of the smart grid network is evaluated using renewable energy sources (SM_RES), and the network has been watched for security evaluation in cloud computing. Multi agent Markov reinforcement learning (MAMRL) is used to analyse the security of the cloud network in the smart grid. In terms of scalability, QoS, energy efficiency, power consumption, prediction accuracy, RMSE, average precision, and data integrity, experimental study is conducted for energy management and network security. Proposed technique attained prediction accuracy 95 %, packet loss rate 77 %, RMSE 88 %, Average precision 85 %, network security analysis 97 %; scalability 95.8 %, QoS 86 %, Power consumption 35 % and energy efficiency 96 %.

Encryption of Card Details Using AES with a 128-bit Key a Secure Approach to Data Protection

Data security is a critical concern in today's digital age, particularly regarding the protection of sensitive information such as cardholder data. This research paper explores the implementation of Advanced Encryption Standard (AES) encryption with a 128-bit key as a means of safeguarding card details. The project demonstrates the encryption and decryption process, ensuring data confidentiality and integrity. Utilizing the pycryptodome library in Python, the AES encryption technique is applied to encrypt card details, offering a secure approach to data protection. The methodology section outlines the AES encryption process, emphasizing key generation, encryption, and decryption. A detailed explanation of the Python implementation highlights the importance of key length and randomness in the encryption process. The results section presents the outcomes of encrypting and decrypting card details using AES encryption, analyzing the efficiency and security of the encryption process. Additionally, potential vulnerabilities and challenges encountered during implementation are discussed, along with their resolutions. In the discussion section, the research findings are interpreted in the context of data security and encryption standards. A comparison between AES encryption and other encryption algorithms is provided, evaluating performance and security aspects. Consideration is given to future research directions in the field of data encryption and security. Overall, this research paper underscores the significance of AES encryption with a 128-bit key for protecting cardholder information. By implementing AES encryption, organizations can enhance data security measures, ensuring the confidentiality and integrity of sensitive data in digital transactions. This abstract provides a concise overview of the research paper, summarizing the objectives, methodology, results, and implications of implementing AES encryption for card data protection. It highlights the importance of data security and encryption techniques in safeguarding sensitive information in digital transactions. Tokenizing cards enhances security, ensures regulatory compliance, boosts customer trust, and streamlines payment processes while reducing liability for organizations. Future developments may involve tokenizing cards even without customer consent. Keywords: Tokenization, Payment systems, Encryption, Fraud prevention

Optimizing Online Business Security with Blockchain Technology

This research discusses the implementation of blockchain technology to improve online business security. The main problem facing online businesses today is their vulnerability to security attacks such as data theft and information leaks. The aim of this research is to optimize online business security by utilizing the advantages of blockchain technology in providing data security, integrity, and transparency. This research method includes a literature review to understand existing online business security frameworks and analysis of possible weaknesses. Next, we designed and implemented a blockchain-based solution to mitigate the identified security risks. Data collection is carried out through security attack simulations and reliability testing of the proposed system. The research results show that the application of blockchain technology significantly increases the security of online businesses, reduces the risk of data theft, and provides a high level of transparency. The conclusions of this research support the view that blockchain can be an effective solution to overcome security problems in the context of online business. This research makes a significant contribution to understanding and applying blockchain technology as an innovative security solution in the context of online business.

Secure Cloud-Based Electronic Health Records: Cross-Patient Block-Level Deduplication with Blockchain Auditing.

In today's data-driven world, the exponential growth of digital information poses significant challenges in data management. In recent years, the adoption of cloud-based Electronic Health Records (EHR) sharing schemes has yielded numerous advantages like improved accessibility, availability, and enhanced interoperability. However, the centralized nature of cloud storage presents challenges in terms of information storage, privacy protection, and security. Despite several approaches that have been presented to ensure secure deduplication of similar EHRs, the validation of data integrity without a third-party auditor (TPA) remains a persistent task. Because involving a TPA raises concerns about the confidentiality and privacy of crucial healthcare information. To tackle this challenge, a novel cloud storage auditing technique is proposed that incorporates cross-patient block-level deduplication while upholding strong privacy protection, ensuring that EHR is not compromised. Here, we introduced blockchain technology to achieve integrity verification, thus eliminating the need for a TPA by providing a decentralized and transparent mechanism. Additionally, an index for all EHRs has been generated to facilitate block-level duplicate checks and employ a novel strategy to prevent adversaries from acquiring original information saved in the cloud storage. The security of the proposed approach is established against factorization attacks and decrypt exponent attacks. The performance evaluation demonstrates the superior efficiency of the proposed scheme in terms of file authenticator generation, challenge creation, and proof verification to other existing client-side deduplication approaches.

A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud

This study presents a newly developed edge computing platform designed to enhance connectivity between edge devices and the cloud in the agricultural sector. Addressing the challenge of synchronizing a central database across 850 remote farm locations in various countries, the focus lies on maintaining data integrity and consistency for effective farm management. The incorporation of a new edge device into existing setups has significantly improved computational capabilities for tasks like data synchronization and machine learning. This research highlights the critical role of cloud computing in managing large data volumes, with Amazon Web Services hosting the databases. This paper showcases an integrated architecture combining edge devices, networks, and cloud computing, forming a seamless continuum of services from cloud to edge. This approach proves effective in managing the significant data volumes generated in remote agricultural areas. This paper also introduces the PAIR Mechanism, which is a solution developed in response to the unique challenges of agricultural data management, emphasizing resilience and simplicity in data synchronization between cloud and edge databases. The PAIR Mechanism’s potential for robust data management in IoT and cloud environments is explored, offering a novel perspective on synchronization challenges in edge computing.

THEORETICAL INSIGHTS INTO SECURING REMOTE MONITORING SYSTEMS IN WATER DISTRIBUTION NETWORKS: LESSONS LEARNED FROM AFRICA-US PROJECTS

This review paper delves into the critical realm of securing remote monitoring systems within water distribution networks, illuminating theoretical insights and practical lessons gleaned from collaborative projects between Africa and the United States. Given the pivotal role of water distribution networks in public health and safety, integrating remote monitoring systems offers substantial benefits in operational efficiency and resource management. However, this integration also introduces significant security vulnerabilities that pose risks to critical infrastructure data's integrity, availability, and confidentiality. Through a comprehensive analysis, this paper explores the multifaceted security challenges inherent in these systems, underscores the importance of robust theoretical frameworks for cybersecurity, and highlights effective security practices derived from international collaboration projects. It proposes principles for designing, implementing, and managing secure systems, emphasizing the necessity of defence-in-depth strategies, the principle of least privilege, and the critical role of continuous monitoring and adaptive response mechanisms. Furthermore, it identifies ongoing challenges and future directions for research, stressing the dynamic nature of cybersecurity threats and the potential of emerging technologies to fortify security measures. The paper advocates for sustained collaborative efforts and research to navigate the evolving landscape of security challenges in water distribution networks, ensuring their resilience against threats.
 Keywords: Remote Monitoring Systems, Water Distribution Security, Cybersecurity Frameworks.

New Hybrid Distributed Attack Detection System for IoT

IoT is expressed as a network of physical objects with applications and various technologies that provide data connection and sharing with various devices and systems over the Internet. Security vulnerabilities in IoT devices are one of the biggest security issues in connecting devices to the internet and collecting and processing user data. These vulnerabilities can lead to increased attacks on IoT devices and malicious use of user data. In this article, we discuss these security problems that arise in IoT systems in detail in distributed systems technology. Distributed systems are increasingly used in the modern computing world. These systems are a structure where multiple independent computers communicate with each other for a common purpose. Distributed system technologies have become more common with the development of internet and cloud computing systems. However, the use of distributed systems has brought with it important security challenges such as security vulnerabilities, access controls and data integrity issues. Therefore, the security of distributed system technologies has been an important focus of work in this area. In this study, information about distributed system technologies and security for IoT is given. The all attack types were classified using ANN, developed RF and hybrid model. In RF, all feature vectors created from all datasets (bank and two financial datasets) were also analyzed separately and the classification performance was examined. In addition, a new RF algorithm based on weight values using the Gini algorithm has been proposed. With this algorithm, the traditional RF algorithm has been developed and the success rates have been increased. In addition, a hybrid method was created by classifying the datasets obtained by RF with ANN. With the hybrid method ANN and the enhanced RF method, its accuracy in detecting normal behaviors and attack types was calculated and the success of the methods was presented comparatively. In addition, the working times of the methods were determined.

A Review of Blowfish Algorithm for Network Security

The introduction of the internet has made safety a top anxiety. And the preceding of safety permits for improved knowledge in the creation of safety tools. Several concerns about safekeeping might have arisen just from the way the internet was set up. Many businesses use firewalls and encryption techniques to protect themselves online. Businesses can design an "intranet" that is protected from potential risks while being linked to the internet. For increased safety, better encryption techniques are needed to retain data integrity. For better encryption, it is essential to consider into explanation a number of issues, including key extent, chunk extent, and encoding proportion. Key Words: Cryptography, Plaintext, Ciphertext, Encryption, Decryption,

Enhancing Patient Consent Management through Blockchain Technology: A Promising Approach for Healthcare Data Security

In contemporary healthcare systems, ensuring patient consent management while safeguarding data privacy and security is a paramount concern. Traditional approaches to managing patient consent often face challenges related to security vulnerabilities, data breaches, and lack of transparency. This research paper proposes a novel approach to address these challenges by leveraging blockchain technology for patient consent management. Blockchain, with its decentralized and immutable ledger, offers a promising solution for securely recording and managing patient consent in healthcare settings. This paper presents a detailed exploration of the concept, design, and implementation of a blockchain-based patient consent management system. Through a comprehensive literature review, methodology description, and evaluation of the proposed system, this research demonstrates the potential benefits of blockchain technology in enhancing patient privacy and data security. The findings of this study contribute to the advancement of healthcare data management practices and provide valuable insights for future research and development in this evolving field. Keywords— Patient consent management, Blockchain technology, Healthcare data security, Privacy protection, Decentralization, Immutable ledger, Transparency, System architecture, Data integrity, Healthcare information management

THE IMPACT OF BIG DATA ON HEALTHCARE PRODUCT DEVELOPMENT: A THEORETICAL AND ANALYTICAL REVIEW

The intersection of big data and healthcare product development has catalyzed transformative shifts in the industry, revolutionizing how medical solutions are conceptualized, designed, and deployed. This theoretical and analytical review explores the profound impact of big data on healthcare product development, elucidating its implications across various facets of the healthcare landscape. Utilizing big data analytics, healthcare stakeholders can harness vast volumes of structured and unstructured data to derive actionable insights. These insights inform evidence-based decision-making processes, driving innovation in product development pipelines. By analyzing real-time patient data, trends, and treatment outcomes, developers gain invaluable insights into disease progression, treatment efficacy, and patient preferences, thus facilitating the creation of tailored, patient-centric solutions. Moreover, big data analytics play a pivotal role in improving patient outcomes and quality of care. Through predictive analytics and machine learning algorithms, healthcare providers can identify at-risk populations, predict disease outbreaks, and personalize treatment plans. This proactive approach enhances preventive care strategies and minimizes healthcare costs by averting complications and hospital readmissions. However, the integration of big data into healthcare product development is not without challenges. Data privacy and security concerns necessitate robust frameworks to safeguard sensitive patient information. Moreover, regulatory compliance frameworks must evolve to accommodate the complexities of big data analytics while ensuring patient safety and data integrity. Despite these challenges, the potential of big data in healthcare product development is vast. By leveraging big data analytics, stakeholders can bridge gaps in healthcare access and equity, tailor interventions to underserved populations, and optimize resource allocation. In conclusion, this review underscores the transformative impact of big data on healthcare product development. By embracing data-driven approaches, stakeholders can drive innovation, enhance patient outcomes, and navigate the evolving healthcare landscape with agility and efficacy.
 Keywords:  Big Data, Healthcare Product Development, Innovation, Patient Outcomes, Data Analytics, Regulatory Compliance.

An Elliptic Curve Menezes–Qu–Vanston-Based Authentication and Encryption Protocol for IoT

The exponential growth of the Internet of Things (IoT) has led to a surge in data generation, critical for business decisions. Ensuring data authenticity and integrity over unsecured channels is vital, especially due to potential catastrophic consequences of tampered data. However, IoT’s resource constraints and heterogeneous ecosystem present unique security challenges. Traditional public key infrastructure offers strong security but is resource intensive, while existing cloud-based solutions lack comprehensive security and rise to latency and unwanted wastage of energy. In this paper, we propose a universal authentication scheme using edge computing, incorporating fully hashed Elliptic Curve Menezes–Qu–Vanstone (ECMQV) and PUF. This approach provides a scalable and reliable solution. It also provides security against active attacks, addressing man-in-the-middle and impersonation threats. Experimental validation on a Zybo board confirms its effectiveness, offering a robust security solution for the IoT landscape.

Abstract 4933: A workflow for cloud-based AI development of multiplex IF image analysis using the OMERO Plus platform

Abstract Background: The advancement of AI in digital pathology is essential for analyzing image data containing billions of cells. This is true for digital pathology workflows using bright-field images, but this is even more critical for multiplex immunofluorescence (mIF) whole-slides due to the increased number of fluorescent channels and bit-depth per pixel. These complexities necessitate improved data management, and efficient visualization for both the mIF image and AI-based analysis results. We present a workflow using OMERO Plus, AWS SageMaker, and Ultivue software for AI model development in mIF. Method: Images were uploaded to AWS S3 and imported to the data management system OMERO Plus, where they were organized into training and validation groups. Annotations: PathViewer was used to place rectangular fields of view (FOV) in locations selected by the pathologist where all positive cells for each target marker were manually annotated. Data Preparation: Using the OMERO Plus API, annotation coordinates, image tiles, and image metadata were extracted and formatted for model training. All training data is under a version control system using Gitlab and Data Version Control tools using AWS S3. Additionally, strict quality checks were performed to ensure data integrity. Training: For both semantic segmentation and cell object detection, the training data was used in a customized training workflow using AWS SageMaker, including modern deep learning architectures, data augmentation, training logging, evaluation, and deployment. All AI models were trained using Pytorch and deployed with the open model format ONNX. Results Visualization: AI segmentation results of cells and regions are visualized using PathViewer as overlays on the original images by converting them to the OME-NGFF label image format that uses Zarr multidimensional arrays. Results: We utilized 58 and 24 mIF images for training and validation, respectively, on 15 markers using ISP Ultivue technology with 1681 and 296 FOVs, encompassing over 60K cells. Using this data and methodology we trained 3 semantic segmentation models and over five object detection models suitable for all twenty-four OmniVUE markers. For instance, the CD3 positive cell detection model, trained with 390 FOVs, achieved highly accurate results: f1-score of 0.915 evaluated on 34 FOVs. Conclusions: The combination of OMERO Plus, PathViewer, AWS SageMaker, and internal tools can successfully address mIF Big Data data management complexities in a cloud environment for digital pathology. The architecture is highly scalable, cost effective, and reliable, enabling a very efficient AI workflow. Citation Format: Ruben Cardenes, Erin Diel, Douglas Wood, Je Lee, Angela Vasaturo, Martin Schulze, Lorenz Rognoni. A workflow for cloud-based AI development of multiplex IF image analysis using the OMERO Plus platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4933.

Emerging technologies in information systems project management

The article discusses emerging technologies in information systems project management. Project management is a modern discipline that began to take shape from 1900 and has evolved and adapted to the needs of society and business. Emerging technologies such as artificial intelligence, blockchain, augmented and virtual reality, and process automation are transforming the way information systems projects are managed. These technologies can be used to analyze large amounts of data, ensure data integrity and security, visualize a project's design and perform virtual testing, and automate tasks to reduce project time and cost. It is important for companies to be aware of these technologies and use them effectively to improve the efficiency and profitability of their projects.

SECURED ORGANIZATIONAL CLOUD STORAGE USING MESSAGE DIGEST ALGORITHM

The Secure Organizational Cloud Storage System is an advanced cloud storage platform designed specifically for organizations that prioritize data security. The platform provides a similar functionality to Google Drive, but with enhanced security features to ensure that sensitive information remains safe from cyber threats. With the Secure Organizational Cloud Storage System, organizations can store and manage their data in a centralized and secure location. Users can easily upload, share, and collaborate on files and documents, and can access them farom anywhere with an internet connection. Secure cloud storage is indispensable in today's data-driven world, providing the convenience of remote data access while mitigating the risks associated with cyber threats and data breaches. By implementing advanced encryption, access controls, data integrity checks and redundancy strategies, secure cloud storage systems offer a comprehensive solution to protect sensitive information in an increasingly interconnected digital ecosystem. This abstract serves as a concise introduction to the importance and key components of secure cloud storage. Cloud service and storage providers offer valuable IT solutions for businesses of all sizes. Originally thought of as more for personal use, cloud storage for business is following in the footsteps of many personal technologies adapted for business purposes. Cloud storage works by storing data on remote servers, where it can be maintained, managed, backed up and accessed remotely. Data stored in the cloud is accessible by any device at any time, as long as permissions are in place. Despite its accessibility, data stored via the cloud is extremely safe and secure. Many people reap the benefits of the cloud for personal reasons, but most businesses have yet to take the leap. Whether your current on-premises data storage seems sufficient, or you simply haven’t taken time to consider cloud storage, reviewing these advantages is a good place to start.

Abstract 2351: Genomics annotation and interpretation in somatic oncology using structured data from a clinical knowledgebase

Abstract Accurate and comprehensive interpretation of genomic variants has become a bottleneck in clinical sequencing applications due to the accelerating precision oncology and biomedical information explosion. This motivated us to build Ephesus, a framework enabling curation of predictive, prognostic and diagnostic evidence for clinical biomarkers in cancers. Currently Ephesus is the primary content source for Roche navify Mutation Profiler (nMP). The Ephesus’ data model ensures adherence to best practices in clinical genomic content curation. Variant classification follows the AMP guidelines for somatic variant interpretation. Variant interpretations are derived from international regulatory approvals and clinical practice guidelines (FDA, EMA, TGA, eVIQ, etc.) and recommendations (NCCN, ESMO). The data model is mapped to a set of relational tables. To enforce data integrity and validity, strategies such as data normalization based on standard nomenclatural ontologies, a submit-review-approval workflow, and biological constraints are adopted. Ephesus is deployed as a web application used by curators inside and outside Roche. The UI allows users to conduct edits, filtering, sorting and bulk operations on entities by attributes. In order to minimize manual effort and maximize content coverage, inference rules are applied before ingesting into nMP. Currently Ephesus is developed for data collection, browsing and summarizing primary entities such as biomarkers, evidence items, genes, variants, variant groups, and drugs. The content from the 2023-Aug snapshot contains 11,596 directly curated biomarker profiles (including biomarker combinations), and 6M+ expanded profiles for 41 major cancer types. To evaluate the clinical reporting value of the curated knowledge, ~160k real cancer patient samples of the AACR GENIE project were queried on Ephesus. Compared with three other major knowledgebases, Ephesus/NMP is observed showing the highest performance. Table 1. Percentage of patients or biomarkers with at least one interpretation from each knowledgebase CGI (2022-10-17) CIVIC(2023-09-01) ClinVar(2023-09-09) NMP(2023-08-11) AACR-Genie-v14.0 Total patient % with any interpretation 25% (40,545) 46% (73,835) 81% (130,882) 89% (142,905) 160,965 biomarker % (including combos) with any interpretation 0.03% (256) 0.06% (526) 13.26% (122,424) 73.79% (681,128) 923,093 Citation Format: Jian Li, Lili Niu, Shuba krishna, Fnu Kinshuk, Martin Jones, Carlos Hernandez, Michael Clark, Sandra Balladares. Genomics annotation and interpretation in somatic oncology using structured data from a clinical knowledgebase [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2351.

Advancing Video Data Privacy Preservation in IoT Networks through Video Blockchain

In the digital age, where the Internet of Things (IoT) permeates every facet of our lives, the safeguarding of data privacy, especially video data, emerges as a paramount concern. The ubiquity of IoT devices, capable of capturing and disseminating vast quantities of video data, introduces unprecedented challenges in ensuring the privacy and security of such information. This article explores the crucial intersection of video data privacy and blockchain technology within IoT networks. It aims to uncover and articulate the unique challenges video data encounter in the IoT ecosystem, such as susceptibility to unauthorized access and the difficulty in ensuring data integrity and confidentiality. By conducting a thorough literature review, this study not only illuminates the intricate privacy challenges inherent in IoT environments but also showcases the immutable, decentralized nature of blockchain as a potent solution. We systematically explore how blockchain-based methods can be pragmatically implemented to fortify video data privacy, scrutinizing the efficacy of these approaches in the IoT context. Through critical assessment, the paper delineates the strengths and limitations of video blockchain solutions, underscoring the transformative potential of blockchain technology as a cornerstone for enhancing data privacy in IoT networks. Conclusively, this work advocates for blockchain as an indispensable tool in the advancement of data privacy measures for video content, thereby reinforcing trust and security in the increasingly connected fabric of our digital world. As IoT applications burgeon, the fusion of blockchain technology with IoT infrastructures promises a robust framework for protecting sensitive video data, heralding a future of enhanced trust and security in our interconnected ecosystem.

Blockchain based Quantum Resistant Signature Algorithm for Data Integrity Verification in Cloud and Internet of Everything

 
 INTRODUCTION: The processing and storage capacities of the Internet of Everything (IoE) platform are restricted, but the cloud can readily provide efficient computing resources and scalable storage. The Internet of Everything (IoE) has expanded its capabilities recently by employing cloud resources in multiple ways. Cloud service providers (CSP) offer storage resources where extra data can be stored. These methods can be used to store user data over the CSP while maintaining data integrity and security. The secure storage of data is jeopardized by concerns like malicious system damage, even though the CSP's storage devices are highly centralized. Substantial security advancements have been made recently as a result of using blockchain technology to protect data transported to networks. In addition, the system's inclusive efficacy is enhanced, which lowers costs in comparison to earlier systems.
 OBJECTIVES: The main objective of the study is to a blockchain-based data integrity verification scheme is presented to provide greater scalability and utilization of cloud resources while preventing data from entering the cloud from being corrupted.
 METHODS: In this paper, we propose a novel method of implementing blockchain in order to enhance the security of data stores in cloud.
 RESULTS: The simulations indicate that the proposed approach is more effective in terms of data security and data integrity. Furthermore, the comparative investigation demonstrated that the purported methodology is far more effective and competent than prevailing methodologies.
 CONCLUSIONS: The model evaluations demonstrated that the proposed approach is quite effective in data security.