Ensure Data Integrity Research Articles

An expert system for privacy-driven vessel detection harnessing YOLOv8 and strengthened by SHA-256

The integration of deep learning with security is undergoing rapid and substantial growth across numerous fields. This combination has led to propelled advancements in securing maritime surveillance data. Applications utilizing deep learning require large amounts of data to deliver precise results. However, Deep learning applications face significant issues like low accuracy, high computational complexity, and GPU utilization. Moreover, security features such as integrity and authenticity are notably absent. Hence, in this paper privacy preserved deep learning-based vessel monitoring system is developed which includes evaluation of integrity and authenticity-based surveillance. This model ensures accuracy without compromising on one way authenticity while also optimizing GPU utilization. Therefore, the YOLOv8 model is integrated with SHA-256 for tracking and classification, also ensuring data integrity and authentication of vessel data. The model is fed with class-balanced, unstructured dataset comprising 693 photo-realistic video sequences. It has three phases which are used for feature extraction and bounding box prediction and also includes CSPDarkNet53 as backbone, Spatial Pyramid Pooling (SPP) as neck layer and detection is done using head layer. They employ a 3 × 3 convolution for better feature extraction. The proposed model provides better performance than other state-of-the-art methods. Specifically, YOLOv8 achieves a 9.3 % increase in precision over YOLOv7.

Streamlining IoT-driven Data Using Blockchain

The integration of Internet of Things (IoT) technology has revolutionized data collection and analytics, creating vast networks of interconnected devices generating large volumes of data. However, this rapid expansion of IoT ecosystems introduces significant challenges related to data security, integrity, scalability, and privacy. Blockchain technology, with its decentralized and immutable nature, emerges as a compelling solution to address these challenges. This paper explores the synergy between IoT and blockchain, examining how blockchain can streamline IoT-driven data by providing secure data exchanges, enhancing transparency, and facilitating scalability. The study investigates the key mechanisms through which blockchain can mitigate common IoT data issues. It considers blockchain's role in ensuring data integrity by utilizing cryptographic methods, enabling traceability through distributed ledgers, and protecting against unauthorized data manipulation. Furthermore, it explores the potential of blockchain-based smart contracts to automate processes and improve interoperability among IoT devices. The paper delves into specific use cases, such as smart cities, demonstrating how blockchain can effectively streamline IoT data across various industries. The findings highlight that while blockchain offers substantial benefits, it also introduces unique challenges, including high energy consumption, latency concerns, and complex integration processes. This concludes with a discussion of Security concerns, Comparison with existing solutions. By addressing these challenges, blockchain can become a pivotal technology in streamlining IoT-driven data, paving the way for more secure, efficient, and trustworthy IoT ecosystems.

A Blockchain-Based Electronic Health Record (EHR) System for Edge Computing Enhancing Security and Cost Efficiency

Blockchain technology offers unique features, such as transparency, the immutability of data, and the capacity to establish trust without a central authority. Such characteristics can be leveraged to support the collaboration among several different software systems operating within the healthcare ecosystem, while ensuring data integrity and make electronic health records (EHRs) more easily accessible. To provide a solution based on blockchain technology, this paper has evaluated the main issues that arise when large amounts of data are expected, i.e., mainly cost and performance. A balanced approach that maximizes the benefits and mitigates the constraints of the blockchain has been designed. The proposed decentralized application (dApp) architecture employs a hybrid storage strategy that involves storing medical records locally, on users’ devices, while utilizing blockchain to manage an index of these data. The dApp clients facilitate interactions among participants, leveraging a smart contract to enable patients to set authorization policies, thereby ensuring that only designated healthcare providers and authorized entities have access to specific medical records. The blockchain data-immutability property is used to validate data stored externally. This solution significantly reduces the costs related to the utilization of the blockchain, while retaining its advantages, and improves performance, since the majority of data are available off-chain.

SECURE & VERIFIABLE E VOTING USING BLOCKCHAIN

In response to the persistent challenges surrounding the aspect of security, verifiability of electronic voting (e-voting) systems, this paper proposes an innovative approach. By incorporating blockchain technology into face recognition authentication, our solution aims to fortify the integrity and transparency of the voting process. Blockchain offers a distributed and immutable ledger, ensuring data integrity, while face recognition authentication offers an additional layer of security. This integrated system establishes a secure and transparent platform for casting and verifying votes, offering a promising solution to the vulnerabilities of traditional e-voting systems. Key Words- Electronic voting, Blockchain technology, Face recognition, Security, Verifiability, Authentication, Transparency, Trust, Democracy.

The Impact of Artificial Intelligence on Microbial Diagnosis.

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

Optimal Method Combining Blockchain and Multi-Agent System to Ensure Data Integrity and Deduplication in the Cloud Environment

Cloud computing (CC) refers to the transmission, storage, and processing of any type of information at a location that is not owned or controlled by the information owner. This information can be stored and accessed anytime, from anywhere, and using any device. However, several challenges threaten the deployment of cloud computing technology within organizations, including user privacy, data integrity, and managing the retention of the vast amount of data that will be stored and analyzed in this environment. In recent years, various data integrity checking systems and data storage mechanisms have been proposed to address these diverse challenges. Nevertheless, some protocols do not guarantee data confidentiality against auditors during the auditing process, while others do not provide batch auditing. Additionally, the majority of these protocols do not support the deduplication technique. Therefore, in this research work, the security features of blockchain technology and those of multi-agent systems (MAS) have prompted us to propose models that enable both data deduplication, saving storage space, and data integrity auditing, efficiently verifying the accuracy of data outsourced to CC environments. Implementation findings indicate that the suggested system is highly feasible and applicable in real-world scenarios.

Enterprise Data Governance : A Comprehensive Framework for Ensuring Data Integrity, Security, and Compliance in Modern Organizations

Enterprise data governance has emerged as a critical function for organizations seeking to maximize the value of their data assets while mitigating risks associated with data management. This article presents a comprehensive framework for implementing effective data governance strategies in modern enterprises. It examines the key components of robust governance structures, including clear policies, well-defined roles and responsibilities, and the establishment of data quality standards. The article explores methods for ensuring regulatory compliance and enhancing data security, emphasizing the importance of risk management and data protection measures. By analyzing case studies across various industries, the article identifies best practices and common challenges in data governance implementation. Additionally, it investigates the impact of emerging technologies such as artificial intelligence and blockchain on data governance practices. The findings underscore the necessity of a holistic approach to data governance that aligns with organizational objectives, fosters a data-driven culture, and adapts to evolving regulatory landscapes. This article contributes to the growing body of knowledge on enterprise data management and provides practical insights for organizations striving to improve their data governance capabilities in an increasingly complex digital environment.

Digital Divide’s Impact on Accounting Information Systems Risks Faced by Financial Auditors: A Jordanian Case Study

The study emphasizes the importance of organizational structure, human capital, and legal controls in reducing accounting system risks. Three research questions explore the impact of the organizational structure, the role of human capital, and the significance of legal controls in risk reduction. Using statistical analysis, 104 questionnaires were distributed, and 76 were collected for the statistical analysis, including simple linear regression. The study reveals significant findings. The organizational structure plays a positive and influential role in establishing sound internal control systems that reduce accounting system risks. Qualified human capital contributes to technical progress, enhancing the accuracy and efficiency of financial audit activities. Robust legal controls effectively identify deviations, ensuring data integrity and compliance with international standards. Recommendations for the JDRC include establishing a clear organizational structure, investing in qualified human capital, and implementing comprehensive legal controls to mitigate accounting system risks. This study contributes to the literature by addressing unique variables and highlighting the risks and limitations of accounting systems within the JDRC. The findings inform organizations in similar contexts, guiding effective risk mitigation strategies and enhancing accounting system performance.

Digital Transformation in the Pharmaceutical Industry: Ensuring Data Integrity and Regulatory Compliance

The pharmaceutical industry is undergoing a significant digital transformation to improve efficiency, productivity, and regulatory compliance. One critical aspect of this transformation is ensuring data integrity, which is essential for maintaining the quality and safety of pharmaceutical products. This article explores the reasons behind the digital transformation in the pharmaceutical industry, focusing on the need for better data integrity. It examines the challenges pharmaceutical companies face in achieving data integrity, such as legacy systems, data silos, resistance to change, cybersecurity risks, and the validation of computerized systems. The article also discusses the benefits of regulatory compliance, including enhanced product quality and patient safety, reduced risk of non-compliance, improved operational efficiency, and increased brand reputation and customer trust. The author proposes strategies for successful digital transformation and data integrity, such as developing a comprehensive roadmap, investing in modern IT infrastructure, implementing data governance and quality management systems, fostering a culture of continuous improvement, and collaborating with technology partners and industry consortia. The article concludes by discussing future trends and opportunities, such as the adoption of blockchain technology, the integration of Internet of Things devices, the use of big data analytics for predictive quality control, and the need for collaboration with regulators to develop industry-wide data integrity standards. This comprehensive review provides valuable insights for pharmaceutical professionals, researchers, and regulators seeking to navigate the complex landscape of digital transformation and data integrity in the pursuit of improved patient outcomes and business success.

A blockchain‐based secure framework for data management

AbstractData management is a crucial requirement due to the autonomous and constrained nature of Unmanned Aerial Vehicles (UAVs), Internet of Things (IoTs), and the aviation domain. The autonomous and restricted nature of these sectors increases the need for a shared, distributed database, strong access control management, consensus in autonomous decision‐making, and effective communication across diverse protocols and devices. This research presents a comprehensive approach and offers a new viewpoint to the field of blockchain while establishing a fundamental baseline for future improvements in data management systems and addressing the shortcomings of previously proposed existing frameworks in order to fulfill the complex needs of secure data management. This study contributes to the advancement of secure and efficient data management systems by implementing robust data monitoring for error detection, ensuring data integrity, and enabling encrypted or anonymous data sharing based on sensitivity levels. Additionally, the integration of diverse devices, enforcement of immutable regulations compliance, and development of permissioned blockchain systems for identity management further enhance the system's capabilities, offering comprehensive solutions for modern data management challenges. In the tests, the proposed framework showed increased successful transactions in all rate controllers. Besides, effect of the validator number on throughput and latency is tested and analyzed thoroughly.

Blockchain-Based Solution for Supply Chain Data Integrity

Data plays a crucial role in today's world, guiding business decisions across various computer- assisted activities. Maintaining the integrity of data is vital, as any tampering could have serious consequences for important business decisions. This concern is particularly significant in cloud computing settings, where data owners have limited control over key aspects like physical storage and access control. Blockchain technology has emerged as an intriguing solution to address data integrity concerns. With its inherent properties, blockchain offers promising avenues for ensuring data integrity. However, some challenges need to be overcome, such as low throughput, high latency, and stability issues, which currently limit the practical implementation of blockchain-based solutions. we focus on a case study from the European SUNFISH project, which aims to develop a secure cloud federation platform for the public sector. We examine the specific data integrity requirements in cloud computing environments and identify the research questions that need to be addressed to implement blockchain-based databases effectively. We start by outlining the open research questions and the inherent difficulties associated with addressing them. Then, we propose a preliminary design for a blockchain-based database tailored to cloud computing environments. This design aims to leverage the strengths of blockchain technology while addressing the challenges unique to cloud computing. By addressing these research questions and proposing practical solutions, we aim to pave the way for the adoption of blockchain-based databases in cloud computing environments. This has the potential to enhance data integrity and security, ultimately benefiting organizations operating in the cloud.

Testing the reliability of an AI-based large language model to extract ecological information from the scientific literature

Artificial intelligence-based large language models (LLMs) have the potential to substantially improve the efficiency and scale of ecological research, but their propensity for delivering incorrect information raises significant concern about their usefulness in their current state. Here, we formally test how quickly and accurately an LLM performs in comparison to a human reviewer when tasked with extracting various types of ecological data from the scientific literature. We found the LLM was able to extract relevant data over 50 times faster than the reviewer and had very high accuracy (>90%) in extracting discrete and categorical data, but it performed poorly when extracting certain quantitative data. Our case study shows that LLMs offer great potential for generating large ecological databases at unprecedented speed and scale, but additional quality assurance steps are required to ensure data integrity.

Blockchain‐based decentralized oracle network framework for identity management in metaverse environment

AbstractManaging identities in metaverse environment is challenging due to the conflicting requirements of ensuring user privacy while enforcing accountability. Users may participate in the metaverse through multiple virtual identities or avatars for different applications/scenarios. Thus, virtual identities must be continuously generated and mapped with real identities. Further, user privacy needs to be protected while not ensuring blanket anonymity to indulge in objectionable or unlawful conduct. Ensuring privacy for the metaverse while still being able to pinpoint users for their actions in the metaverse is a significant research issue. Solving the aforementioned issue, we propose a third‐party identity management solution for the metaverse based on creating a decentralized oracle network (DON) providing identity authentication and mapping service. The proposed identity management framework is metaverse platform agnostic, ensuring users' privacy on the platform while still being able to uncover their real identities if warranted. The proposed framework is viable as evidenced by experimental results of DON and blockchain implementation. The DON based framework leverage the benefits of blockchain technology and introduces immutability, transparency, and traceability into the the metaverse environment. This ensures data integrity and minimizes the frequency of cyber attacks in the metaverse ecosystem.

Digital twins and dynamic NFTs for blockchain-based crowdsourced last-mile delivery

In this paper, we propose a blockchain-based crowdsourcing framework leveraging Digital Twins (DTs) and dynamic Non-Fungible Tokens (NFTs) for a transparent last-mile delivery. Existing works propose package delivery through crowdsourced workers managed by centralized or blockchain-based platforms. While the centralized platforms suffer from data security and reliability vulnerabilities, the blockchain-based platforms lack real-time and transparent package status monitoring. The availability of immediate access to status updates and early warnings regarding package storage conditions greatly contributes to ensuring the safe delivery of sensitive packages and preventing spoilage or further damage. As a solution, we propose a decentralized crowdsourcing platform for last-mile delivery utilizing Ethereum smart contracts and DTs for both the packages and the workers. The contracts are leveraged to facilitate interactions between the requesters and workers, and securely execute the task allocation mechanism, which relies on a computed Quality-of-Service (QoS) metric. The package DTs are proposed to oversee the package status and promptly alert workers’ DTs when delivery conditions are violated. To ensure data integrity and prevent malicious alterations, the DTs’ data is securely stored using NFTs. Upon task completion, a calculated Quality-of-Delivery (QoD) acts as a feedback loop in updating workers’ reputations. The proposed allocation mechanism is benchmarked against distance-based and preferences-based allocation mechanisms where our framework improves the average QoS by 76%, average QoD by 22%, and quality of selected workers by 11%. In addition, the DTs are implemented to verify alert generation through a use case. We provide a detailed cost analysis of deployed smart contracts, along with insights into current limitations and proposed mitigation strategies.

Women Safety System Using ARM Micro controller and Arduino

This abstract presents a comprehensive women's safety device leveraging modern technology for effective emergency response. The device integrates various components including the Neo6M GPS module, module, Arduino Nano microcontroller, push button for emergency alert, , pulse oximeter MAX30100, and temperature sensor LM35. The Arduino Nano serves as the central processing unit, orchestrating data collection, processing, and transmission.Incorporating a Python Flask WebApp and ThingSpeak platform, the device ensures real-time alerts and cloud storage capabilities. The Flask WebApp facilitates user interaction and customization, enabling users to set preferences and receive notifications. ThingSpeak enables seamless data logging and retrieval from the cloud, ensuring data integrity and accessibility.Upon activation, the device initiates location tracking via the GPS module and triggers an emergency alert through the module. Simultaneously, vital signs such as pulse rate and temperature are monitored using the MAX30100 pulse oximeter and LM35 temperature sensor respectively. These data are transmitted to the Flask WebApp and ThingSpeak platform for real-time monitoring and alert generation.This innovative solution aims to provide women with a reliable, multifunctional safety device capable of prompt emergency response and continuous monitoring, ultimately enhancing their safety and well-being in various settings. Key Words: Safety system, ARM micro-controller, Arduino, GPS, Alerting, Web-Application.

Decoding Consumer Behavior: Predictive Analytics of Social Media Advertising Effectiveness

Decoding Consumer Behavior: Predictive Analytics of Social Media Advertising Effectiveness

Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis.

Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations. Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations. The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL's versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance.

An Underwater Source Location Privacy Protection Scheme Based on Game Theory in a Multi-Attacker Cooperation Scenario.

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.

Campus Connect - Bridging the Academic Community with Blockchain-Powered Decentralization

Abstract: In response to the evolving landscape of education in the digital era, CampusConnect emerges as a transformative blockchain-based decentralized application. This platform seeks to redefine the college experience by establishing a secure and collaborative environment for students, faculty, and campus communities. Leveraging blockchain technology, CampusConnect ensures data integrity, transparency, and decentralization, addressing the limitations of traditional social media platforms in meeting the unique needs of educational institutions. The objectives of this innovative initiative include enhancing communication, promoting resource sharing, improving campus event management, and offering a specialized academic network.

Optimization model for multibeam sounding line deployment based on genetic algorithm

Multibeam sounding technology is a cutting-edge method used for measuring underwater topography, widely applied across various fields. The actual seafloor terrain often exhibits significant fluctuations, leading to challenges such as insufficient coverage in shallow areas and excessive overlap in deep-water zones, resulting in inefficient operations. Rational selection of line spacing helps improve measurement efficiency and ensures data integrity. In this paper, taking the 2023 National College Student Mathematical Modeling Competition Problem B as a background, we conduct a thorough analysis of the variation patterns of the coverage width of multibeam sounding strips with respect to various parameters. We establish models for ocean depth and coverage width, determining how the strip coverage width changes with distance and the angle between survey lines in different scenarios. Finally, employing genetic algorithms and differential methods, we identify decision variables, objective functions, and constraints to formulate a nonlinear programming model. Through iterative searches, we obtain an optimal sounding line deployment plan that maximizes coverage of the target sea area while keeping the overlap rate below 20%. The calculated missed measurement error is approximately 32.774%, and the overlap rate is around 10.477%, indicating a high level of model accuracy.