Data Access Management Research Articles

Blockchain-Enabled Pension System Innovations: A Hungarian Case Study on Business Process Management Integration

This paper explores the integration of Business Process Management (BPM) with blockchain technology to enhance pension systems, using Hungary as a case study. Specifically, it addresses scientific challenges related to data access management, regulatory compliance, and system scalability within blockchain-based pension frameworks. This study investigates how BPM can improve the transparency, efficiency, and security of blockchain applications in pension administration by optimizing workflows and automating compliance with regulations such as GDPR. By analyzing operational flow diagrams and implementing architectural models, this paper presents an innovative approach to pension management, demonstrating significant improvements in service quality and operational efficiency. Findings from this research provide empirical evidence of the benefits of BPM-enhanced blockchain systems, offering insights applicable to pension systems beyond the Hungarian context, including examples from other countries.

The Role of Technology Integration in Public Sector Accounting Fraud Prevention Efforts

This research aims to determine the role of public-sector accounting information technology in preventing fraud in public-sector organizations. The study was a literature review based on articles suitable for further analysis of the research topic. The study found that there are ways to prevent fraud that, include technology and prevention strategies. The technology strategy includes leveraging data protection officers for fraud prevention, IoT access, data access management, timely reporting, and real-time data management. The prevention strategy is to develop a fraud detection system using machine algorithms, data mining, and meta-learning techniques.

Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure

Background: The global healthcare system faces challenges in diagnosing and managing lung and colon cancers, which are significant health burdens. Traditional diagnostic methods are inefficient and prone to errors, while data privacy and security concerns persist. Objective: This study aims to develop a secure and transparent framework for remote consultation and classification of lung and colon cancer, leveraging blockchain technology and Microsoft Azure cloud services. Dataset and Features: The framework utilizes the LC25000 dataset, containing 25,000 histopathological images, for training and evaluating advanced machine learning models. Key features include secure data upload, anonymization, encryption, and controlled access via blockchain and Azure services. Methods: The proposed framework integrates Microsoft Azure’s cloud services with a permissioned blockchain network. Patients upload CT scans through a mobile app, which are then preprocessed, anonymized, and stored securely in Azure Blob Storage. Blockchain smart contracts manage data access, ensuring only authorized specialists can retrieve and analyze the scans. Azure Machine Learning is used to train and deploy state-of-the-art machine learning models for cancer classification. Evaluation Metrics: The framework’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, demonstrating the effectiveness of the integrated approach in enhancing diagnostic accuracy and data security. Results: The proposed framework achieves an impressive accuracy of 100% for lung and colon cancer classification using DenseNet, ResNet50, and MobileNet models with different split ratios (70–30, 80–20, 90–10). The F1-score and k-fold cross-validation accuracy (5-fold and 10-fold) also demonstrate exceptional performance, with values exceeding 99.9%. Real-time notifications and secure remote consultations enhance the efficiency and transparency of the diagnostic process, contributing to better patient outcomes and streamlined cancer care management.

Blockchain Based Decentralized Storage Drive

The "Decentralized Storage Drive" project introduces a secure, blockchain-based platform for decentralized data storage and sharing. By integrating the Interplanetary File System (IPFS) for decentralized storage, the platform ensures that data remains immutable and resistant to censorship. Solidity smart contracts deployed on the Ethereum blockchain provide robust mechanisms for access control, allowing users to manage data ownership and permissions without relying on centralized authorities. A React-based front- end interface simplifies user interaction, enabling seamless data upload and access management. This project addresses critical issues related to data privacy, security, and ownership, offering a user-friendly solution that empowers individuals with greater control over their digital assets. Throughout the development of this project, a focus on security, user control, and decentralization has been paramount. The rigorous testing of smart contracts and the integration of secure authentication methods ensure that the platform is both reliable and resilient to potential threats. The "Decentralized Storage Drive" represents a significant advancement in the field of decentralized applications, demonstrating the practical application of blockchain technology in everyday data management. Key Words: Decentralized Storage, Blockchain Technology, IPFS(InterPlanetary File System), Smart Contracts, Data Integrity, Access Control

Integrating Cloud Services with Mobile Applications for Seamless User Experience

In the modern digital era, the integration of cloud services with mobile applications has emerged as a pivotal strategy for delivering a seamless and dynamic user experience. This approach not only enhances the functionality of mobile applications but also ensures that users can access services and data in real-time, regardless of their location. As mobile devices continue to dominate the technology landscape, the demand for responsive, scalable, and user-centric applications has skyrocketed. Cloud services, with their inherent capabilities such as storage, computing power, and advanced analytics, provide the backbone for developing applications that meet these demands. The integration of cloud services with mobile applications offers numerous advantages. It allows for the offloading of heavy computational tasks to cloud servers, thereby preserving the limited processing power and battery life of mobile devices. This capability enables mobile applications to perform complex tasks such as real-time data processing, machine learning inference, and multimedia content rendering without compromising user experience. Additionally, cloud services facilitate the synchronization of data across multiple devices, ensuring that users have consistent access to their data and settings regardless of the device they are using. This is particularly important in scenarios where users switch between devices, such as moving from a smartphone to a tablet or laptop. Moreover, the use of cloud services in mobile applications enhances security and reliability. Cloud providers typically offer robust security features, including data encryption, identity and access management, and continuous monitoring, which are essential for protecting sensitive user data. By leveraging these features, mobile applications can offer a higher level of security than would be feasible with on-device storage and processing. Furthermore, the reliability of cloud services ensures that applications remain available and functional even during peak usage times or when the user is in a location with poor network connectivity. Through techniques such as caching and offline access, cloud-integrated mobile applications can continue to operate smoothly even when real-time access to cloud resources is temporarily unavailable.

Comparative analysis of security models in cloud platforms

This article is dedicated to the importance of data security on cloud platforms, highlighting the subject relevance in an era where threats to information security are becoming increasingly complex and sophisticated. The primary focus is a comparative analysis of security models employed by leading cloud platforms such as Amazon Web Services, Microsoft Azure, Google Cloud Platform, IBM Cloud, and Oracle Cloud. The research covers various crucial security aspects, including data encryption, identity and access management, monitoring mechanisms and incident response. The article sheds light on current trends and technologies in cloud data protection, including the use of artificial intelligence and machine learning to enhance threat detection efficiency and the implementation of confidential computing and blockchain technologies to improve data protection. Issues of compliance with legal requirements and data security standards are discussed, along with recommendations for organisations to optimise information protection in the cloud environment.

A lattice-based data sharing functional encryption scheme with HRA security for IoT

In the dynamic and resource-constrained landscape of IoT, securing sensitive data access is paramount. This paper introduces an innovative access control scheme (ACPRE) blending conditional attribute-based proxy re-encryption with inner product encryption, tailored for IoT security needs. ACPRE integrates attribute-based conditions, expands plaintext space, and restricts proxy privileges, bolstering access control. Leveraging inner product encryption, it confines decrypted information to inner product values, ensuring data confidentiality. By limiting proxies’ transformation abilities, ACPRE prevents unauthorized data alterations. It offers a secure, flexible solution for data access management, allowing only entities with specific attributes to utilize inner product values. ACPRE’s lattice-based construction enables resistance against quantum attacks and achieves honestly re-encryption attacks security, fostering robust, privacy-aware IoT ecosystems. Detailed security proofs and experimental validation underscore its efficacy.

A qualitative interview study to determine barriers and facilitators of implementing automated decision support tools for genomic data access

Data access committees (DAC) gatekeep access to secured genomic and related health datasets yet are challenged to keep pace with the rising volume and complexity of data generation. Automated decision support (ADS) systems have been shown to support consistency, compliance, and coordination of data access review decisions. However, we lack understanding of how DAC members perceive the value add of ADS, if any, on the quality and effectiveness of their reviews. In this qualitative study, we report findings from 13 semi-structured interviews with DAC members from around the world to identify relevant barriers and facilitators to implementing ADS for genomic data access management. Participants generally supported pilot studies that test ADS performance, for example in cataloging data types, verifying user credentials and tagging datasets for use terms. Concerns related to over-automation, lack of human oversight, low prioritization, and misalignment with institutional missions tempered enthusiasm for ADS among the DAC members we engaged. Tensions for change in institutional settings within which DACs operated was a powerful motivator for why DAC members considered the implementation of ADS into their access workflows, as well as perceptions of the relative advantage of ADS over the status quo. Future research is needed to build the evidence base around the comparative effectiveness and decisional outcomes of institutions that do/not use ADS into their workflows.

Secure and fine-grained access control of electronic health record using SAPCP-ABE technique in cloud

This study explores how Electronic Health Records (EHR) might be transformed in the context of the rapid improvements in cloud computing and IoT technology. But worries about sensitive data security and access management when it moves to large cloud provider networks surface. Even if they are secure, traditional encryption techniques sometimes lack the granularity needed for effective data protection. We suggest the Secure Access Policy – Ciphertext Policy – Attribute-based Encryption (SAPCP-ABE) algorithm as a solution to this problem. This method ensures that only authorized users may access the necessary data while facilitating fine-grained encrypted data exchange. The three main phases of SAPCP-ABE are retrieval and decoding, where the system verifies users’ access restrictions, secure outsourcing that prioritizes critical attributes, and an authenticity phase for early authentication. Performance tests show that SAPCP-ABE is a better scheme than earlier ones, with faster encryption and decryption speeds of 5 and 5.1 seconds for 512-bit keys, respectively. Security studies, numerical comparisons, and implementation outcomes demonstrate our suggested approach’s efficacy, efficiency, and scalability.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things (IoT) is a significant trend in technology and a requirement for national strategic development. With the deepening digital transformation of the power grid, China’s power system has initially built a power IoT architecture comprising a perception, network, and platform application layer. However, owing to the structural complexity of the power system, the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment, diverse IoT protocol access methods, high concurrency of network communications, and weak data security protection. To address these issues, this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT, comprising cloud, pipe, edge, and terminal parts. It further reviews and analyzes the key technologies involved in the power IoT, such as the unified management of the physical model, high concurrent access, multi-protocol access, multi-source heterogeneous data storage management, and data security control, to provide a more flexible, efficient, secure, and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Advancing Data Migration and Virtualization Techniques: ETL-Driven Strategies for Oracle BI and Salesforce Integration in Agile Environments

In the modern business landscape, seamless integration of data across platforms is crucial for enhancing operational efficiency and decision-making. This paper proposes an ETL-driven approach for data migration between Oracle Business Intelligence (BI) and Salesforce, emphasizing the importance of minimizing downtime and ensuring data accuracy during the migration process. The focus is on leveraging Extract, Transform, Load (ETL) strategies to streamline data movement, thereby ensuring a smooth transition between legacy systems and modern cloud-based platforms, critical for industries like finance and enterprise resource planning (ERP). The proposed methodology mitigates the challenges of data inconsistency, errors, and extended downtime, which can negatively impact business operations. Additionally, this paper reviews the role of data virtualization in the integration of Oracle BI and Salesforce, offering a more flexible and scalable solution for accessing real-time data across different systems without the need for extensive data replication. By enabling virtualized data layers, businesses can enhance operational decision-making by providing real-time insights while maintaining system agility. This approach aligns with Agile workflows, enabling businesses to respond rapidly to changing market conditions and customer demands. The paper outlines the current state of data virtualization practices within Oracle BI and Salesforce environments, identifying key benefits such as improved data access, reduced latency, and cost-effective infrastructure management. Furthermore, it discusses future directions for data virtualization, particularly focusing on real-time analytics, predictive modeling, and AI-driven decision support, which are essential for competitive business strategies. The ETL-driven migration and data virtualization framework proposed in this paper serves as a foundation for optimizing data flow, improving system integration, and enhancing business intelligence capabilities. By incorporating these strategies within Agile environments, organizations can ensure more efficient operations, enhance decision-making, and stay ahead of the competition in a data-driven world.

Harnessing H5P for Asynchronous Active Learning

Active learning, as defined by Bonwell and Eison (1991), involves students in doing things and thinking about what they are doing. Extensive research has highlighted the numerous benefits of active learning, including increased attendance rates (Kozanitis & Nenciovici, 2022), improved learning outcomes (Ruiz-Primo et al., 2011), reduced achievement gaps for underrepresented students (Haak et al., 2011; Theobald et Al., 2020), higher-order thinking skills, and enhanced student performance (Freeman et al., 2014). In comparison to traditional lectures, any form of active learning has proven to be beneficial to student learning (Schneider & Preckel, 2017). However, most research on active learning has focused predominantly on face-to-face synchronous classrooms, with little attention to active learning in online asynchronous environments. This presents an opportunity to explore design solutions that promote active learning in these asynchronous settings where students lack real-time interactions with educators and peers. The advent of HTML5 (HyperText Markup Language version 5) or H5P has the potential to revolutionise active learning in online learning settings. As a free and open-source content creation tool, H5P offers various interactive and engaging content types that enable students to interact directly with course material in meaningful ways. This interactivity encourages students to participate actively in their learning process rather than passively obtaining information from lectures. Drawing from two case studies conducted at an Australian university, this presentation highlights how H5P can be used to enhance active learning in online asynchronous courses. The first case study investigates the design of H5P training modules for a platform that supports researchers in humanities and social sciences in their sensitive data access management. This platform is part of a nation-wide project involving 12 partnering institutions and government agencies in Australia. The second case study explores the implementation of H5P learning activities in a Vietnamese language course over three years. In both cases, H5P is used as a plug-in within the Moodle learning platform. Data collection for these case studies included interviews with educators and learning designers, Moodle reports, and participant feedback surveys. The findings from the thematic analysis and the learning analytics revealed substantial benefits of using H5P for active learning, along with the technical and pedagogical challenges encountered during the design process. The presentation features exemplary cases and insights from the case studies to suggest H5P design solutions that foster active participation, reflective thinking, and knowledge construction for students in online asynchronous settings.

CACHE DATA REPLACEMENT POLICY BASED ON RECENTLY USED ACCESS DATA AND EUCLIDEAN DISTANCE

Data access management in web-based applications that use relational databases must be well thought out because the data continues to grow every day. The Relational Database Management System (RDBMS) has a relatively slow access speed because the data is stored on disk. This causes problems with decreased database server performance and slow response times. One strategy to overcome this is to implement caching at the application level. This paper proposed SIMGD framework that models Application Level Caching (ALC) to speed up relational data access in web applications. The ALC strategy maps each controller and model that has access to the database into a node-data in the in-Memory Database (IMDB). Not all node-data can be included in IMDB due to limited capacity. Therefore, the SIMGD framework uses the Euclidean distance calculation method for each node-data with its top access data as a cache replacement policy. Node-data with Euclidean distance closer to their top access data have a high priority to be maintained in the caching server. Simulation results show at the 25KB cache configuration, the SIMGD framework excels in achieving hit ratios compared to the LRU algorithm of 6.46% and 6.01%, respectively.

Data Governance for Real-World Data Management: A Proposal for a Checklist to Support Decision Making

ObjectivesReal-world data (RWD) and real-world evidence (RWE) can provide extensive information on healthcare for use in health technology assessment and decision making. Nevertheless, there is a lack of consensus surrounding the appropriate data governance (DG) practices for RWD/RWE. Data sharing is also a large concern, especially considering evolving data protection regulations. Our objective is to propose recommendations for international standards of evaluating the acceptability of RWD governance practices. MethodsAfter reviewing the literature, we created a checklist targeting DG practices for RWD/RWE. We then carried out a 3-round Delphi panel, including European policy makers, health technology assessment experts, and hospital managers. The consensus for each statement was measured and the checklist adjusted accordingly. ResultsThe literature review identified the main topics regarding RWD/RWE DG practices: data privacy and security, data management and linkage, data access management, and the generation and use of RWE. Members of the Delphi panel (21 experts/25 invited) were presented a total of 24 statements related to each of the topics. Experts demonstrated a progressive level of consensus and importance ratings in all topics and to most statements. We suggest a refined checklist in which the statements rated less important or with less consensus have been removed. ConclusionsThis study suggests how the DG of RWD/RWE could be qualitatively evaluated. We propose checklists that could be used by all RWD/RWE users to help ensure the quality and integrity of RWD/RWE governance and complement data protection law.

Data workflows and visualization in support of surveillance practice.

The Swedish National Veterinary Institute (SVA) is working on implementing reusable and adaptable workflows for epidemiological analysis and dynamic report generation to improve disease surveillance. Important components of this work include: data access, development environment, computational resources and cloud-based management. The development environment relies on Git for code collaboration and version control and the R language for statistical computing and data visualization. The computational resources include both local and cloud-based systems, with automatic workflows managed in the cloud. The workflows are designed to be flexible and adaptable to changing data sources and stakeholder demands, with the ultimate goal to create a robust infrastructure for the delivery of actionable epidemiological information.

BENFEP: a quantitative database of benthic foraminifera from surface sediments of the eastern Pacific

Abstract. Benthic foraminifera are important components of the ocean benthos and play a major role in ocean biogeochemistry and ecosystem functioning. Generating ecological baselines for ocean monitoring or biogeographical distributions requires a reference dataset of recent census data. Moreover, the information from their modern biogeography can be used to interpret past environmental changes on the seafloor. In this study, we provide the first comprehensive quantitative benthic foraminifera database from surface sediments of the eastern Pacific (BENFEP). Through the collation of archival quantitative data on species abundance and their homogenization according to the most recent taxonomic standards, we are able to provide a database with 3077 sediment samples, corresponding to 2509 georeferenced stations of wide geographical (60∘ N–54∘ S) and water depth (0–7280 m) coverage. The quantitative data include living, dead, and living plus dead assemblages obtained from 50 published and unpublished documents. As well as describing the data collection and subsequent harmonization steps, we provide summarized information on metadata, examples of species' distributions, potential applications of the database, and recommendations for data archiving and publication of benthic foraminiferal data. The database is enriched with meaningful metadata for accessible data management and exploration with R software and geographical information systems. The first version of the database (BENFEP_v1, Diz et al., 2022a, https://doi.org/10.1594/PANGAEA.947086) is provided in short and long format, and it will be upgraded with new entries and when changes are needed to accommodate taxonomic revisions.

A Data Integrity and Security Approach for Health Care Data in Cloud Environment

Healthcare digitalization has turned out to be a significant paradigm in the advancement of medical sector. Resultant, there is production of huge volume of patient’s digital information. Moreover, storing and safeguarding such information tends to become a critical point of concern. The cloud technology assures to be an effective mechanism towards handling and distributing healthcare information by attending the concerns pertaining to data integrity, security and privacy. The significant benefits and accessibility offered by the cloud technology has immensely raised the storage of healthcare information. It’s highly important that the sensitive healthcare data residing in the cloud is completely secured amidst the healthcare providers and the cloud server. There has been a rising concern associated with secured data access within the cloud environment. That is, the cloud framework must assure a robust authentication control mechanism which allows the healthcare users to confidently share medical information across it. Towards accomplishing the same, a secured and light weight data access management system has been recommended that promises data Integrity and Security. Moreover, the proposed system offers the feature of authority control authorization and authentication mechanism for safeguarding the health-related data residing and circulating within the cloud. A CIAM (Customer identity and access management) helps in secured Data Access Control and OAuth 2.0 aids in providing right privilege with authorisation. Also, by the means of Progressive Attribute Based Encryption approach, sensitive medical information can be encrypted from the cloud storage in a secure manner. The proposed light weight framework yields in efficient, reliable and stable output within the cloud.

Intelligent compression then encryption scheme for resource constrained sustainable and smart healthcare environment

With the latest and emerging developments of artificial technologies, ubiquitous computing techniques become necessary in day to day lives, particularly in healthcare. Smart healthcare systems involve electronic health records (EHR) sharing system that considerably shares the medical data and delivers proper aid to the patients and research communities. At the same time, the smart healthcare system is a resource constraint environment where large amount of healthcare data needs to be stored on the cloud server. Therefore, data compression can significantly reduce a large amount of data transmission and storage process, thereby achieving effective memory and resource utilization. Compression then encryption (CTE) is an effective way to achieve effective resource utilization and security in the smart healthcare environment. Encryption transforms the EHR data to an illegible format offering considerable authentication of the digital form of medicinal data. With this motivation, this paper designs a novel compression then encryption model for secure healthcare data storage and access management (CTE-SHDSA) technique in cloud server. The proposed model involves two main stages namely compression and encryption. Initially, neighboring indexing sequence (NIS) is employed for the data compression process. The NIS-BWT technique makes use of the correlation among the neighboring bits for reducing the amount of redundant data transmission. Besides, an enhanced artificial butterfly optimization with signcryption (EABO-SC) technique is applied to proficient encrypt the compressed data. The EABO algorithm is applied to optimally choose the encryption keys of the SC technique. The extensive set of simulations was performed on benchmark medical datasets and the outcomes are inspected under several evaluation parameters. The experimental values highlighted the superior performance of the presented method on the recent state of art methods.

Open science tools: Supporting hands-on creation of the \"digital extended specimen\"

As a biodiversity informatics community, we have mobilised and interconnected a wide array of information, including specimen collections, published literature and metadata resources, which compile facts about collections and the people that work with them. We have defined data standards to facilitate data interoperability and tools development. Along with colleagues in allied research disciplines, we have helped to develop training resources, enabling researchers to automate routine tasks like data access and reference management. We have also started to explore how we could realise the vision of the digital extended specimen, which would integrate specimens and associated data across multiple research infrastructures, allowing the investigation of wider scale research questions. How this would be achieved is still the subject of discussion and experimentation: an open community will support a diverse range of approaches. In the construction of the digital extended specimen, we can envision useful activities operating at very different scales: from large scale computational processes run at (or between) research infrastructures, to an ecosystem of lightweight tools that support link construction in context, closer to researchers. A toolset enabling in-context link construction could play a similar role to Open Refine (which has been effective at democratizing data linking between different sources), and supply valuable training data for the development of machine learning approaches. We will review the use of Open Refine in the biodiversity informatics (and wider research) communities and examine the resources and working practices that facilitated the adoption of this tool. We will showcase work towards “an extensible notebook for open science”, and we aim to open a discussion on how a link-aware editor for semi-structured data plus standard open science tools (i.e., those covered by training resources such as software, data and author carpentry) could be viewed as a lightweight alternative to traditional document production—just as Open Refine is a viable alternative to many traditional spreadsheet use cases. Our aim is to enable researchers to develop the digital extended specimen at research time, but without being prescriptive about their workflow. We will conclude by discussing how this effort supports open science, showing how researchers are open to access the data needed to explore their area of study and to form their hypotheses, using well recognised entities (specimens, names, people, institutions, citations etc), represented in data standards and accessed via open APIs - but also open to choose how they organise their work. In the authors own domain (botany), such a tool will be fundamental to e-taxonomic undertakings to build an online reference system in which all known plant species are described, as well as to significant acceleration of parts of the taxonomic process to address the biodiversity crisis.

Structured Data Storage for Data-Driven Process Optimisation in Bioprinting

Bioprinting is a method to fabricate 3D models that mimic tissue. Future fields of application might be in pharmaceutical or medical context. As the number of applicants might vary between only one patient to manufacturing tissue for high-throughput drug screening, designing a process will necessitate a high degree of flexibility, robustness, as well as comprehensive monitoring. To enable quality by design process optimisation for future application, establishing systematic data storage routines suitable for automated analytical tools is highly desirable as a first step. This manuscript introduces a workflow for process design, documentation within an electronic lab notebook and monitoring to supervise the product quality over time or at different locations. Lab notes, analytical data and corresponding metadata are stored in a systematic hierarchy within the research data infrastructure Kadi4Mat, which allows for continuous, flexible data structuring and access management. To support the experimental and analytical workflow, additional features were implemented to enhance and build upon the functionality provided by Kadi4Mat, including browser-based file previews and a Python tool for the combined filtering and extraction of data. The structured research data management with Kadi4Mat enables retrospective data grouping and usage by process analytical technology tools connecting individual analysis software to machine-readable data exchange formats.