Data Access Services Research Articles

Pushing health data access boundaries - adapting NHS governance for 3 challenges: clinical free-text, safe commercial access and AI development

This presentation will demonstrate expansions to TRE governance frameworks developed to facilitate safe data access in three advanced areas: commercial access, clinical free-text, and innovation with AI/Machine Learning. NHS research governance frameworks - originally set up for clinical trials and expanded into the TRE and secondary data use space – are not an immediate fit for advanced techniques in data science research. Over 2 years, public, expert and user engagement approaches were utilised to support developing frameworks within DataLoch, a Health and Social Care TRE and data access service in South-East Scotland. Surveys, workshops and continuous user and data controller feedback allowed the testing of tools and assessments to adapt traditional NHS governance structures. These frameworks utilise the Five Safes framework to make NHS data controllers supportive of innovative research in the public and health service’s benefit. The presentation will summarise the challenges faced for one TRE and researchers working to support these areas, and the frameworks that are now are being tested within an NHS Scotland Health Board. The development and implementation demonstrates that governance structures can be adapted collaboratively and paves the way for more flexible TRE governance to allow the use of NHS patient data to remain legally and ethically robust while supporting advanced techniques of analysis.

AI-HybridChain: Picturized authentication and DRL based access control method with secure two fold revocation for ensuring cloud computing security

The Internet of Things is susceptible to seepage of private information during the data sharing. To circumvent this problem, access control and secure data sharing have been familiarized in cloud- IoT which is prevalent now days due to it storage and data access services. An intrusion detection implementation defends the veracity of the data sharing confining record are stored in a data base by recognizing when something changed unpredictably so that security and privacy are major anxieties. Therefore, it is crucial to develop the security apparatus that perceive and mitigate any feasible attack in cloud security services. In this paper, we proposed secure access control method with two-fold revocation for ensuring cloud computing security by hybrid blockchain (AI-Hybrid chain). Initially, all the users and services are registered their credentials to Secure Agent (SA) and provides image-based user authentication. Then, SA provides a long-term secret key which is generated by Improved Advanced Encryption Standard (IAES) algorithm, both user and service credentials are encrypted and stored in cloud chain. After that, gateway verifies the incoming requests are legitimate or illegitimate. For verifying the incoming packets, the proposed work utilizes Di-Fuzzy Inference System (Di-FIS) based on effective metrics. Further, legitimate request is optimally classified as sensitive and non-sensitive request for achieving secure service allocation using Tomtit Flock Optimization algorithm (TFO) based on optimal features. Once the service is allocated to users, we perform novel access control mechanism based on role and attribute of the users, the trust can be validated using Transfer Learning based Double Deep Q Network (TL-DDQN). Furthermore, virtual firewall is deployed in the cloud environment for analyzing the user storage request traffic as normal validated using Transfer Learning based Double Deep Q Network (TL-DDQN). Furthermore, virtual firewall is deployed in the cloud environment for analyzing the user storage request traffic as normal and malicious based on Contractive Residual Network (Con-ResNet). The implementation of proposed work is conducted by Network Simulator – 3.26 and the performance of the proposed AI-Hybrid chain model is itemized based on various performance metrics in terms of attack detection rate, overhead, success ratio, throughput, traffic rate, delay, response time and unauthorized access.

Attribute-based key management for patient-centric and trusted data access in blockchain-enabled IoMT

Internet of Medical Things (IoMT) provides real-time data collection and access services to medical entities by connecting their devices to Internet. However, within such a complex architecture, achieving patient-centric data access still presents trustworthiness challenges of transmission security and usage reliability. Ciphertext-policy attribute-based encryption (CP-ABE) and blockchain are both the promising primitives that support patients with secure access control over their data. Nevertheless, both have their unique key management issues. In this paper, we propose a novel patient-centric and trusted data access (PCT-DA) system in blockchain-enabled IoMT. Firstly, we develop an online/offline encryption and verifiable outsourced decryption CP-ABE scheme to relieve the computation pressure on patients and users. Secondly, we design a flexible attribute-based key management mechanism to overcome the security drawbacks of encryption and attribute keys, such as single point of failure, key escrow and privacy leakage. Finally, we build a trusted integrity audit contract to guarantee the incentive fairness between patients and users in access services. Moreover, theoretical analyses prove that the PCT-DA system not only achieves confidentiality, integrity, traceability and forward/backward security, but also guarantees static security under random oracles. Simulation experiments indicate that our proposal reduces the time cost by approximately 14.10 seconds over state-of-the-art and is more practical in blockchain-enabled IoMT system.

Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data

The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface.

A secure cross-domain authentication scheme based on threshold signature for MEC

The widespread adoption of fifth-generation mobile networks has spurred the rapid advancement of mobile edge computing (MEC). By decentralizing computing and storage resources to the network edge, MEC significantly enhances real-time data access services and enables efficient processing of large-scale dynamic data on resource-limited devices. However, MEC faces considerable security challenges, particularly in cross-domain service environments, where every device poses a potential security threat. To address this issue, this paper proposes a secure cross-domain authentication scheme based on a threshold signature tailored to MEC’s multi-subdomain nature. The proposed scheme employs a (t,n) threshold mechanism to bolster system resilience and security, catering to large-scale, dynamic, and decentralized MEC scenarios. Additionally, the proposed scheme features an efficient authorization update function that facilitates the revocation of malicious nodes. Security analysis confirmed that the proposed scheme satisfies unforgeability, collusion resistance, non-repudiation and forward security. Theoretical evaluation and experimental simulation verify the effectiveness and feasibility of the proposed scheme. Compared with existing schemes, the proposed scheme has higher computational performance while implementing secure authorization updates.

Application of big data technology in electromechanical operation and maintenance intelligent platform

Abstract Aiming at the data preprocessing requirements and label data cost issues arising from the intelligent operation and maintenance of electromechanical equipment, this article mainly studies structured data cleaning methods and fault prediction algorithms for a small number of label samples. First, this article introduces the overall architecture of the intelligent operation and maintenance system for electromechanical equipment. Second, based on the electromechanical equipment operation and maintenance data access service, data cleaning, and fault prediction, this article constructs an electromechanical equipment intelligent operation and maintenance platform based on Kafka message queue, Spark cluster, and other components, and introduces the functional composition of the system in detail. Finally, the article describes the functions of each component of data access service, data cleaning, and fault prediction in detail. To address the cost issue associated with sufficient labeled sample data for data analysis, we propose a semi-supervised learning algorithm, IF-GBDT, based on improved independent forests and Gradient Boosting Decision Tree. The independent forest algorithm supplements labels for unlabeled data based on the learning results of a small number of labeled samples. We also use the gradient lifting tree algorithm to train the model based on the new tag data set for fault prediction, thereby reducing the impact of lack of tags on the accuracy of the prediction model. Experiments show that this method improves classification accuracy and has good adaptability and concurrency performance for a small number of tags.

Citizens’ utilization of open government data portals in China: a comparative case study of supply vs demand

PurposeThe rising volume of open government data (OGD) contrasts with the limited acceptance and utilization of OGD among citizens. This study investigates the reasons for citizens’ not using available OGD by comparing citizens’ attitudes towards OGD with the development of OGD portals. The comparison includes four OGD utilization processes derived from the literature, namely OGD awareness, needs, access and consumption.Design/methodology/approachA case study in China has been carried out. A sociological questionnaire was designed to collect data from Chinese citizens (demand), and personal visits were carried out to collect data from OGD portals (supply).FindingsResults show that Chinese citizens have low awareness of OGD and OGD portals. Significant differences were recognized between citizens’ expectations and OGD portals development in OGD categories and features, data access services and support functions. Correlations were found between citizens’ OGD awareness, needs, access and consumption.Originality/valueBy linking the supply of OGD from the governments with each process of citizens’ OGD utilization, this paper proposes a framework for citizens’ OGD utilization lifecycle and provides a new tool to investigate reasons for citizens’ not making use of OGD.

The Effect of Customer Equity on Brand Commitment and Brand Switching on Samsung Smartphone Users

One of the benefits of smartphones in terms of software is the availability of data access services. The large selection of products offered by smartphone companies allows brand switching to occur. This brand shift occurred because of the decline in the community's economy due to Covid-19 which made people tend to buy goods at cheaper prices, although cheap, they can be utilized and used according to the functions and needs of consumers. This research is important because it is so that business owners know what things make consumers commit and switch to a brand. This research was conducted in Badung Regency, and the number of samples used was 110 respondents. Data was collected using a questionnaire survey approach. Data were analyzed using PLS (Partial Least Square) data analysis technique. The results of this study indicate that Customer Equity has a positive and significant effect on brand switching. Customer Equity has a positive and significant effect on brand commitment. Brand commitment has a negative and significant impact on brand switching. Brand commitment is positively and significantly able to mediate customer equity and brand switching.

Intrusion detection system in distributed cloud computing: Hybrid clustering and classification methods

Cloud Computing is popular nowadays due to its storage and data access services. Security and privacy are prime concerns when network threats increase. Cloud computing offers organizations and enterprises a scalable, flexible, and cost-effective infrastructure to store data on the Web. An anomaly-based IDS implementation protects the integrity of the data in a database by identifying and quarantining records when something appears to have changed unexpectedly. Machine learning based clustering and classification methods are used for anomaly based IDS attack classification and scalability in advanced networking environments. Machine learning is a fast, efficient, and adaptable approach to develop intrusion detection models that can deal with emerging threats, i.e., known and unknown attacks (including zero-day attacks). This paper proposes an efficient Hybrid clustering and classification models for implementing an anomaly-based IDS for malicious attack type classifications such as normal (no intrusion), DoS, Probe, U2R, and R2L using threshold-based functions, and the results are tested with two different threshold values (e), 0.01 & 0.5. The experiments have been performed on two tested datasets, namely, NSL-KDD and KDDcup99. Detection rate, False alarm ratio, and accuracy have been used to study the performance of the proposed methodology. After applying the proposed approach, the K-means with random forest has been shown at two different threshold values to have a better classification accuracy, detection rate, and false alarm rate of 99.85%, 99.78% and 0.09% on the NSL-KDD dataset and 98.27%, 98.12% and 2.08% respectively on the KDDcup99 dataset.

Secure and Efficient Item Traceability for Cloud-Aided IIoT

Cloud computing is an essential technique to provide item traceability for industrial internet of things (IIoT) systems by providing item data sharing services. However, a malicious cloud server may prevent industrial participants from acquiring accurate traceability of items by providing inconsistent item data. To fix this issue, we propose Acics, an item data consistency auditing scheme in untrusted cloud services for cloud-aided IIoT systems. Acics presents two variants named S-Acics and L-Acics. S-Acics enables industrial participants to audit item data consistency for each item and circularly play the auditing role. L-Acics further enables industrial participants to audit item data consistency for a sampled subset of items while resisting data selection attack via a new separated storage mechanism. Finally, Acics integrates a fair payment mechanism built on smart contract to incentivize the cloud server to provide consistent item data access service for industrial participants. The experiment results show that our solution can audit item data consistency with reasonable cost.

The COBIT 5 Analysis on World Muslim Scientists Index Web Application

The web mobile aims to access data services using mobile devices connected to a cellular telecommunications network. One of the knowledge that needs to be obtained by the wider community, especially the younger generation of Islam, is knowledge of World Muslim Scientists. This World Muslim scientist himself has a very great power to influence the younger generation to be more creative and innovate accompanied by their pride in their predecessors. Information on the World Muslim Scientists Index needs to be packaged to make it attractive. Web Mobile can introduce the World Muslim Scientists Index which is interesting and attractive in delivering information. The problem in several previous studies related to application design is that the application design is not audited for its governance, so it is at risk of not being planned and managed properly. Therefore, in this study, the authors develop the design and implementation of the World Muslim Scientists Index, accompanied by a governance audit in its design using the COBIT 5 Framework method in the PO (Plan and Organize) domain. The result of this research is that the design and management of this application is already at maturity level 4, namely Manage, which means that it is well planned and managed.

Analysis of Signal Quality, Voice Service, and Data Access on Telkomsel And Indosat Providers in Pakisjaya District

Quality in communication is very important in activities in this modern era. It is expected that the expected network quality against network quality is actually not expected. Measurement of network performance is important to refer for users to choose the right operator for service providers to improve the quality of their service Drive Test Method is a method used to measure cellular network performance. The purpose of this study is to study the results of research between service providers. The parameters used in this analysis are Receive Signal Code Power (RSCP), Cell ID, Signal Noise Ratio (SNR), Upload Rate, and Download Rate. This parameter is used for the measurement process of the Drive Test method. While the tool used to collect the value of each Drive Test parameter by using an Android-based application, GNet Track Pro. The measurement results with GNet Track Pro are then processed with the Google Earth application as a measurement area. Research data collected in this analysis were 60 cases, with each village 20 data collected to continue processing with the Drive Test method. In this study, Telkomsel chose to excel in voice services with 150 successful numbers of 150 attempted calls. While Indosat excels in providing data access services and special signals in the village of Tanjungbungin.

Secure Data Sharing: Blockchain-Enabled Data Access Control Framework for IoT

As Internet-of-Things (IoT) service becomes richer, data sharing among different IoT systems gets popular. The traditional IoT system provides data storage and access service with the central cloud, which faces serious trust and security challenges. To provide a cross-system data sharing service, we adopt blockchain to build a multicenter data management (DM) framework and construct a trustable environment for data sharing. As regards to a security problem, attribute-based encryption (ABE) has been applied to the IoT system, but it still relies on the central server. Therefore, we design an ABE algorithm that could be used for multicenter scenario and shift DM to blockchain instead of a central server. Moreover, IoT devices always cannot afford complex encrypt computations as they have limited computing resource. To solve this, we design an obfuscating policy to shift encryption computations to the cloud instead of terminals. In this way, IoT devices could encrypt data with low computation cost. Security analysis and simulations prove that the algorithm we designed could reduce computation burdens of IoT terminals in data encryption and decryption phases effectively and safely.

Towards ubiquitous data access service of 3D part library in cloud scheme with host independence

Most current part libraries are developed and deployed in specific usage scenario or CAD systems, which inevitably brings obstacles in information sharing and exchanging of parts. To reduce repeated development and provide a uniform platform for designers in different sites, a 3D part library in cloud scheme is presented in this paper. Host programs with predetermined specification can access the part data through an adapter according to their customized requests with uniform interfaces, which constructs a ubiquitous service. To realize host independence, the part models are created in a modeler with native ACIS, and then, they are converted into 3D files in different formats for practical needs; finally, these models are imported into CAD systems or other platforms in real designs. The framework is composed of three components, namely, as PLS (part library service) provider, PLS adapters, and PLS hosts. PLS provider is the kernel of 3D data access service in cloud scheme, while PLS adapters serve as the bridges that connect PLS provider and hosts, and the PLS can be grafted on various applications including current mainstream CAD systems as an embedded module or run on the websites or even mobile terminals. The PLS provider is deployed and maintained on cloud, and designers can acquire remote part information within a local ongoing project. In the detailed construction of this part library, diversiform knowledge in part parameters and structures is implanted to define the geometry and rule constraints for the 3D modeling, with which the backstage has the ability of conveniently editing the information in the part library for better upgrade and contrapuntally services. This concept has been implemented within a PaaS framework to provide the ubiquitous 3D part data accessing route, which has been successfully applied in a large number of enterprises, and accumulates considerable practical cases.

Secure and Authenticated Data Access and Sharing Model for Smart Wearable Systems

Contrary to the public cloud storage services that impose users to accept the security restrictions delivered by the service provider, users in the private cloud benefit from self-managed, authenticated data access services. However, this may lead to security issues. A critical challenge is the provision of secure and authenticated data storage for the data owner. Moreover, the data owner should be able to access the stored data and share it with others in a controlled manner. In this article, a secure and authenticated data storage, access, and sharing model is proposed for private cloud storage, which has three components. The data storage component provides the user with secure storage of information. The data sharing component enables sharing the stored data under the control of the data owner. The data access component enables authenticated access to the cloud storage. The security analysis demonstrates that the model is secure against various attacks. The scheme is validated to be secure via the Scyther tool, BAN Logic, and in Random Oracle Model. The performance analysis regarding the computation and communication cost via simulation in OMNeT++ show that it obtains the required security goals and efficiency of computation and communication, compared to the related methods.

Approach towards a Holistic Management of Research Data in Planetary Science—Use Case Study Based on Remote Sensing Data

In the planetary sciences, the volume of remote sensing data and derived research products has been continuously increasing over the last five decades. The amount and complexity of data require growing sophistication in data analysis, data management, and data provision targeted at a growing research community. In order to efficiently manage and facilitate the reuse of research data and to provide stable and long-term access, sustainable research data solutions are needed. We here present a prototype for structured storage, management, and visualisation of planetary research data and discuss the particular benefits, as well as challenges of such an information system for data management, for establishing data references by cross-linking information, and for improving the visibility of data products. The prototype is a co-development of two research institutes of the German Aerospace Center (DLR) and is based on two components: the Earth Observation Center (EOC) Geoservice, which constitutes an infrastructure providing data storage and management capabilities, as well as an interface compliant with collaborative and web-based data access services, and the Environmental and Crisis Information Systems (UKIS), a framework for the implementation of geoscientific web applications.

Ensuring the ethical use of big data: lessons from secure data access

Big data holds great potential for research and for society, large volumes of varied data can be produced and made available to researchers much faster compared to ‘traditional’ data. Whilst this potential is recognized, there are ethical concerns which users of big data must consider. With the volume and variety of information in big data, comes a greater risk of disclosure. Researchers and data access services working with highly detailed and sensitive, secure data have grappled with this for many years. The sector has developed both ethical frameworks and statistical disclosure control techniques which could be utilized by those working with big data. We discuss the challenges, present some of the frameworks and techniques and conclude with recommendations for secure data access of big data.

The unified system for storing and accessing geophysical data. Traditions and new approaches

The features of the development of data collection systems within the Information Processing Center (IPC) of the Geophysical Survey of the Russian Academy of Sciences (GS RAS) are presenting. Historical information is given, including technical details related to the systematization of the archive and the evolution of geophysical data formats. The historical, territorial features, as well as the experience of deploying such information systems within the Federal Research Center of the Unified State Social Service of the Russian Academy of Sciences and abroad are taken into account. A new concept of building an information system is proposed, which takes into account the requirements for scalability, reproducibility at various objects and the use of standard software. A lot of work was done to form a homogeneous archive of waveforms and an inventory of metadata for seismic stations, which resulted in the possibility of including the GS RAS in the international centers for processing geophysical information based on FDSN. Creation of a distributed collection and processing system using a cloud service allows abstracting from the territorial features of collecting and storing geophysical information, which increases the performance of the data access service and the degree of technical readiness of key system nodes.

Towards Environmental RF-EMF Assessment of mmWave High-Node Density Complex Heterogeneous Environments.

The densification of multiple wireless communication systems that coexist nowadays, as well as the 5G new generation cellular systems advent towards the millimeter wave (mmWave) frequency range, give rise to complex context-aware scenarios with high-node density heterogeneous networks. In this work, a radiofrequency electromagnetic field (RF-EMF) exposure assessment from an empirical and modeling approach for a large, complex indoor setting with high node density and traffic is presented. For that purpose, an intensive and comprehensive in-depth RF-EMF E-field characterization study is provided in a public library study case, considering dense personal mobile communications (5G FR2 @28 GHz) and wireless 802.11ay (@60 GHz) data access services on the mmWave frequency range. By means of an enhanced in-house deterministic 3D ray launching (3D-RL) simulation tool for RF-EMF exposure assessment, different complex heterogenous scenarios of high complexity are assessed in realistic operation conditions, considering different user distributions and densities. The use of directive antennas and MIMO beamforming techniques, as well as all the corresponding features in terms of radio wave propagation, such as the body shielding effect, dispersive material properties of obstacles, the impact of the distribution of scatterers and the associated electromagnetic propagation phenomena, are considered for simulation. Discussion regarding the contribution and impact of the coexistence of multiple heterogeneous networks and services is presented, verifying compliance with the current established international regulation limits with exposure levels far below the aforementioned limits. Finally, the proposed simulation technique is validated with a complete empirical campaign of measurements, showing good agreement. In consequence, the obtained datasets and simulation estimations, along with the proposed RF-EMF simulation tool, could be a reference approach for the design, deployment and exposure assessment of the current and future wireless communication technologies on the mmWave spectrum, where massive high-node density heterogeneous networks are expected.

DiSSCo e-Services to Serve Global Community Needs

DiSSCo (Distributed System of Scientific Collections) is a research infrastructure (RI) under development, which will provide services for the global research community to support and enhance physical and digital access to the natural history collections in Europe. These services include training, support, documentation and e-services. This talk will focus on the e-services and will give an overview of the current status, roadmap and first results as an introduction to the next talks in the session, which focus on some of the services in more detail and the standards work undertaken in Biodiversity Information Standards (TDWG) to enable them. The RI community will provide the envisioned e-services, which will use the novel FAIR Digital Object (FDO) infrastructure serving digital specimens from the European collections. The infrastructure will provide integrated data analysis, enhanced interpretation, annotation and access services for community curation and visualisation. The FDO infrastructure enables specimen data to be (re-)connected with genomic, geographical, morphological, taxonomic and environmental information through the digital specimen, making them Digital Extended Specimens. A large number of user stories have been collected through the DiSSCo-linked projects ICEDIG, SYNTHESYS+ and DiSSCo Prepare, to guide which e-Services to build and what functionality to provide. These user stories are publicly available in a github repository. The e-services are developed based on the user stories and prioritization provided by collection providers and the scientific community. A variety of mechanisms are used to collect input: surveys, workshops, roundtables and workpackage meetings, and feedback from users that have already been using beta versions of some of the services. DiSSCo aims to become operational in 2026 but several of the services are already being piloted or implemented. Experimental services and demonstrators are publicly available through DiSSCo Labs for testing and feedback. By connecting the specimen data with derived and related information in a FAIR way (Findable, Accessible, Interoperable and Reusable), the e-services will accelerate biodiversity discovery and support novel research questions. The FDO infrastructure has a data model that also integrates the PROV Ontology (PROV-O), which allows for the e-services to capture activities and improve the visibility of researcher contributions. This vision towards FAIR and high quality data is essential for community curation of the specimen data and making better use of the limited number of experts available. To provide the DiSSCo e-services in a FAIR way, the data derived from the natural history collections in Europe needs to be integrated as one virtual collection. The data has to be findable and accessible as soon as it is being created for services like a Specimen Data Refinery prior to publication in a facility like GBIF (Global Biodiversity Information Facility). This requires new standards for describing collections and specimen data. Standards being created to fill these gaps are TDWG CD (Collection Descriptions) and TDWG MIDS (Minimum Information about a Digital Specimen). The DiSSCo e-Services vision brings the data, standards, and processes together to serve the user community.