Data-sharing Framework Research Articles

Optimal data-driven strategy for in-house and outsourced technological innovations by open banking APIs

AbstractOpen banking is a customer consent-driven data-sharing framework to maintain interoperability among financial and non-financial institutions through secure application programming interfaces. Traditional retail banks are losing their competitive edge against digital banks, FinTech, and BigTech firms due to a higher outflow of customer account data than inflow. These firms capitalize on open banking data to launch innovative products and introduce “ready-to-deploy” Banking-as-a-Service platforms for end-to-end banking operations without the need to build full-scale infrastructures. Therefore, banks must reshape data-driven strategies to stay competitive. This research proposes a decision-support tool to select optimal digital strategies using the Strategic Value Index (SVI), a metric derived from analyzing multiple strategic objectives of bank stakeholders. The SVI minimizes implementation time and costs while maximizing operational action importance to a balanced digital strategy that combines both in-house and outsourced technological developments. The importance of operational action is quantified using evidential reasoning with fuzzy logic to address the challenge of aggregating incomplete and ambiguous banking data and assessments from multiple stakeholders. The theoretical approach is validated through a real application in a Latin American bank, and its findings are globally transferable. Cost and time data were sourced from public repositories, as made available accessible through government-mandated disclosures. The sensitivity analysis revealed that hybrid in-house and outsourced development is more flexible in meeting tight timelines and budget constraints. The combined approach is more cost-effective, time-efficient, and aligned with the internal needs of banks compared to either entirely in-house or fully outsourced models.

A framework for sharing of clinical and genetic data for precision medicine applications.

Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients' biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues. Here we present a secure framework that allows immutable storage, querying and analysis of clinical and genetic data using blockchain technology. Our platform allows clinical and genetic data to be harmonized by combining them under a unified framework. It supports combined genotype-phenotype queries and analysis, gives institutions control of their data and provides immutable user access logs, improving transparency into how and when health information is used. We demonstrate the value of our framework for precision medicine by creating genotype-phenotype cohorts and examining relationships within them. We show that combining data across institutions using our secure platform increases statistical power for rare disease analysis. By offering an integrated, secure and decentralized framework, we aim to enhance reproducibility and encourage broader participation from communities and patients in data sharing.

Blockchain-Based User Authentication and Data-Sharing Framework for Healthcare Industries

Blockchain-Based User Authentication and Data-Sharing Framework for Healthcare Industries

A confidentiality preserved data sharing framework for decision support in the maritime law enforcement

Effective Maritime Law Enforcement (MLE) requires a responsive and high-quality decision-making support system, which typically relies on a wide collection of maritime intelligence from various entities. However, data-sharing among these entities encounters challenges arising from confidentiality concerns and security laws. This paper proposes a technical data-sharing framework to address this issue by introducing the use of homomorphic encryption. This method enables data-sharing while preserving the confidentiality of sensitive information. To validate the feasibility of the proposed framework, a practical evaluation is conducted. This evaluation offers evidence and supporting data to demonstrate the effectiveness and practicality of the suggested approach.

Chemistry in a graph: modern insights into commercial organic synthesis planning

We present graph databases as a modern solution for storing and accessing chemical knowledge. This approach is demonstrated in commercial route selection and holds the potential to create a universal data-sharing framework for chemistry.

Practical Byzantine Fault Tolerance-Enhanced Blockchain-Enabled Data Sharing System: Latency and Age of Data Package Analysis

Data timeliness, privacy, and security are key enablers for data-sharing systems to support time-sensitive and mission-critical systems and applications. While blockchain-enabled data sharing frameworks can offer reliable security and privacy when properly implemented, the timeliness of data and the related latency are important issues that can limit the adoption of blockchain in large-scale mission-critical applications. This paper thus carried out a performance analysis of the blockchain-enabled data-sharing framework from latency and data age perspectives to investigate the suitability of blockchain technology in data sharing systems. To achieve this, the uniqueness of such systems such as transactions validation latency, transaction generation rate, waiting time, blockchain-appending rate, and overall communication latency were jointly studied. The communication latency was characterized following the spatiotemporal modeling approach. We further adopted the practical Byzantine fault tolerance (PBFT) consensus protocol due to its well discussed suitability in large-scale data sharing applications and captured the validation stages of such a PBFT scheme using the Erlang distribution of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> . Simulations results show that various influential system parameters must be carefully considered when adopting blockchain technology in time-sensitive data sharing applications. This will guide the adoption of blockchain technology in various data sharing applications and systems.

VA Big Data Science: A Model for Improved National Pandemic Response Present and Future.

The US Department of Veterans Affairs (VA) enterprise approach to research (VA Research) has built a data-sharing framework available to all research teams within VA. Combined with robust analytic systems and tools available for investigators, VA Research has produced actionable results during the COVID-19 pandemic. Big data science techniques applied to VA's health care data demonstrate that medical research can be performed quickly and judiciously during nationwide health care emergencies. We envision a common framework of data collection, management, and surveillance implemented in partnership with other health care agencies that would capture even broader, actionable, and timely observational data on populations, while providing opportunities for enhanced collaborative research across agencies. This model should be continued and expanded through the current COVID-19 and future pandemics. Extending the achievements of VA Research in the COVID-19 pandemic to date, we advocate national goals of open science by working toward a synergistic national framework of anonymized, synchronized, shared health data that would provide researchers with potent tools to combat future public health crises.

Accountable Bench-to-Bedside Data-Sharing Mechanism for Researchers

We present a trustworthy mechanism for sharing, reusing, and repurposing data to address the challenge of the costly and time-consuming effort needed to bring an innovative idea from the bench (basic research) to the bedside (clinical level). Even though researchers may generate a solution on their own, other aspects of research, including peer review and dissemination of data/results, have an inherent social component. Compared with the centralized mechanisms of data-sharing (and the subsequent reuse and repurposing), many, if not all, aspects of these processes can be decentralized by using blockchain (for full decentralized and autonomous control), coupled with provenance (to ascertain how and where the resources have been leveraged) and incentive semantics (for characterizing how researchers would be rewarded for their contributions). By capturing metadata details at each step of the workflow, data will be easier to audit, verify, and merge with related datasets. It is common in settings where data is either sensitive or valuable (or both) to have formal data use agreements or sometimes less formal rules for reuse, which we have captured in smart contracts. A key innovative aspect of this work is the departure from the traditional natural language–based data use agreements to make these agreements more computable, resulting in enhanced usability and interoperability by a broader community. We have developed the Data Sharing Ontology, a structured vocabulary to guide various incentive mechanisms and criteria used in the decentralized protocol we introduced with smart contracts. Our solution can track data reuse, provide peer reviews on accountable data reuse, and report any violations, thus providing metrics for measuring data producers’ impact on reward structures and research measures. We introduce the SCIENCE-index designed to incentivize data-sharing in scientific research, which builds upon prior indices used in academic research, such as the h-index and the data-index. The SCIENCE-index is publicly available and automatically calculated by a smart contract based on an individual’s data sharing, reuse, and responsible stewardship activities. By incentivizing fair and honest data-related activities, the SCIENCE-index can help improve the speed, cost, and quality of scientific research. As an example application of this decentralized data-sharing framework, we demonstrate how this approach could radically improve the quality and the efficiency of scientific output in the setting of COVID-19 research data-sharing from the National COVID Cohort Collaborative (N3C).

Multi-chain blockchain based secure data-sharing framework for industrial IoTs smart devices in petroleum industry

Industry 4.0 efficiently enhances the manufacturing process in many aspects such as increasing system performance, productivity, cost reduction, and building large-scale systems with enable autonomous operation. Numerous heterogeneous devices and sensors are connecting to the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional schemes adopted centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and latency making them unsuitable for resource-constrained IoTs devices. In our work, we proposed a decentralized, low-power consumption, fast, scalable, secure, privacy-preserving, and trusting architecture-based private blockchain network/smart contract and interplanetary file system for the petroleum industry in Iraq. Our scheme improves blockchain performance by adopting the incremental aggregator subsector commitment instated of Merkle tree and a multi-chain proof of rapid authentication as consensus algorithms, which are specially designed for industrial IoTs applications. It provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability, traceability, and information backtracking. In addition to addressing the blockchain storage limitation and eliminating third-party dependencies by adopting an interplanetary file system (IPFS). Also provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm with internet friendly ARM Cortex-M33 Microcontroller as clear in the experimental results. Moreover, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the petroleum industry as an industrial application area, and provides an efficient framework that resists common cyber-security attacks by achieving data integrity, availability, and confidentiality.

PPChain: A Blockchain for Pandemic Prevention and Control Assisted by Federated Learning.

Taking COVID-19 as an example, we know that a pandemic can have a huge impact on normal human life and the economy. Meanwhile, the population flow between countries and regions is the main factor affecting the changes in a pandemic, which is determined by the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, this is restricted by the differences in prevention and control policies in different areas and privacy issues, such as how a patient's personal data from a medical center cannot be effectively combined with their passenger personal data. This prevents more precise airport control decisions from being made. To address this, this paper designed a novel data-sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment uses a CPU i7-12800HX and uses Docker to simulate multiple virtual nodes. The model is deployed to run on an NVIDIA GeForce GTX 3090Ti GPU. The experiment shows that the relationship between a pandemic and aircraft transport can be effectively explored by PPChain without sharing raw data. This approach does not require centralized trust and improves the security of the sharing process. The scheme can help formulate more scientific and rational prevention and control policies for the control of airports. Additionally, it can use aerial data to predict pandemics more accurately.

Blockchain-based privacy-preserving data-sharing framework using proxy re-encryption scheme and interplanetary file system

Blockchain-based privacy-preserving data-sharing framework using proxy re-encryption scheme and interplanetary file system

Users' Information Security Awareness of Home Closed-Circuit Television Surveillance

Closed-circuit television (CCTV) surveillance cameras are widely used in public and private areas around the world. It is primarily used for tracking individuals and preventing criminal activities. It is necessary to balance the benefits of video surveillance and the risks it poses to individuals' right to privacy. The existing studies raised privacy issues of installing CCTV in public places. However, there is a lack of studies investigating users’ awareness of information security and privacy limitations in installing CCTV in private places. Thus, in this study, the author evaluated users' information security awareness of the value of CCTV and other forms of video surveillance. In-person interviews were conducted in Riyadh province, Kingdom of Saudi Arabia. A total of 77 individuals responded to the interview. A qualitative analysis was conducted to evaluate the participants’ perception of CCTV usage. The outcome of the analysis revealed four themes: Privacy invasion, privacy awareness, dilemmas in implementing security, and preventive measures. The findings revealed that the participants required strict privacy policies for installing CCTV video monitoring systems in private areas. In addition, they understood that CCTV is effectively reducing the fear of crime. The research contributes to understanding users' general awareness of information security and offers the necessary steps to protect the user's privacy in a CCTV surveillance environment. In addition, a data-sharing framework is recommended to share the data in a secure environment. Furthermore, researchers can utilize the study findings in conducting further similar investigative studies.

IoT Data Sharing Platform in Web 3.0 Using Blockchain Technology

As Internet of Things (IoT)-based systems become more prevalent in the era of data-driven intelligence, they are prone to some unprecedented challenges in terms of data security and systems scalability in an era of context-sensitive data. The current advances in IoT-driven data sensing and sharing rely on third-party sources of information (TTPs) that gather data from one party, then transmit it to the other. As a result of TTPs’ involvement, such IoT systems suffer from many issues including but not limited to security, transparency, trust, and immutability as a result of the involvement of the company. Moreover, a multitude of technical impediments, such as the computation and storage poverty of IoTs, privacy concerns, and energy efficiency, enhances the challenges for IoTs. To address these issues of IoT security, we propose a blockchain-enabled open IoT data-sharing framework based on the potential of the interplanetary file system (IPFS). We have used a case study-based approach to evaluate the proposed solution. It is submitted that the proposed scenario is implemented by building smart contracts in Solidity and deploying them on the local Ethereum test network, using the Solidity programming language. With the implementation of smart contracts on the blockchain for access roles in IoT data sensing, the proposed solution advocates for a blockchain-based approach to data security for IoT systems that makes use of smart contracts for access roles.

RDSF—Responsive Data-Sharing Framework for User-Centric Internet of Vehicles Assisted Healthcare Systems

RDSF—Responsive Data-Sharing Framework for User-Centric Internet of Vehicles Assisted Healthcare Systems

Secure Multi-Party Household Load Scheduling Framework for Real-Time Demand-Side Management

Renewable power sources are being increasingly incorporated into distribution networks. Therefore, demand-side management (DSM) has become more critical for improving system reliability. Currently, decentralized real-time DSM is practicable based on home energy management system (HEMS). However, coordinating these HEMSs is difficult because DSM customers may not wish to communicate with each other due to competition and privacy contents. A new peak may even emerge in the aggregator if HEMSs shift their loads without proper coordination. Hence, a secure multi-party household load scheduling framework was proposed in this study to ensure encrypted data sharing between HEMSs based on homomorphic encryption (HE) technology. In order to solve decentralized real-time DSM by directly using additive HE data in this proposed framework, a reinforcement learning (RL) method, namely boosting tree-based deep Q-network, was developed to be trained on a distributed algorithm. The results of case studies revealed that the proposed data-sharing framework outperformed the conventional DSM in shaving peak loads of the aggregator, whereas the electricity cost of each customer did not increase. Moreover, the proposed RL method protected the privacy of users and obtained a similar result compared with no-privacy-preserving RL methods.

A Secure Data Sharing Scheme in Community Segmented Vehicular Social Networks for 6G

The use of aerial base stations, AI cloud, and satellite storage can help manage location, traffic, and specific application-based services for vehicular social networks. However, sharing of such data makes the vehicular network vulnerable to data and privacy leakage. In this regard, this article proposes an efficient and secure data sharing scheme using community segmentation and a blockchain-based framework for vehicular social networks. The proposed work considers similarity matrices that employ the dynamics of structural similarity, modularity matrix, and data compatibility. These similarity matrices are then passed through stacked autoencoders that are trained to extract encoded embedding. A density-based clustering approach is then employed to find the community segments from the information distances between the encoded embeddings. A blockchain network based on the Hyperledger Fabric platform is also adopted to ensure data sharing security. Extensive experiments have been carried out to evaluate the proposed data-sharing framework in terms of the sum of squared error, sharing degree, time cost, computational complexity, throughput, and CPU utilization for proving its efficacy and applicability. The results show that the CSB framework achieves a higher degree of SD, lower computational complexity, and higher throughput.

Blockchain Concepts on Computer Vision With Human-Computer Interaction and Secured Data-Sharing Framework

Presently, the technological developments in the field of human-computer interaction (HCI) have shown that developers are developing cognitive vision systems that provide normal and effective operating mechanism for smart sensors, and the privacy should be maintained in the course of data transfer. Blockchain technology received significant interest to remove third-party business providers, to introduce HCI rapidly, and for secure information sharing in the network. Therefore this paper presents blockchain assisted cognitive vision systems for human-computer interaction and secured data sharing (BCVS-DS) framework. Cognitive vision systems use the information from various sensors that is used to handle and joined by blending techniques. The secured data sharing (SDS) method is flexible and efficiently manages permission by spreading various user characteristics to multiple authorization centers. Experimental results are tested for BCVS-DS by AVEC dataset. BCVS-DS achieves the highest classification accuracy of 94.32%.

Cross‐organisational data sharing framework based on blockchain‐probes

With the development of information technology, various social fields have gradually realised informatisation and digitisation, and today's society has entered the era of big data. A large amount of data exists in different departments and systems. At present, cross-organisational data sharing is often realised by point-to-point authorisation. However, there are still some threats in the management and control schedule for the whole region. To realise secure and credible data sharing, this paper proposes a cross-organisational data-sharing framework based on Blockchain-Probes using distributed digital identity technology. By using a star data link of the complex network, our system realises the synchronisation of heterogeneous data sources by using a data probe as the inter-central transmission carrier to link different data sources. The proposed framework is applied to the real scenario; compared with the traditional method, the efficiency and accuracy are improved, and the total cost is reduced by 60%. The framework could be employed in multiple complex system application scenarios such as transportation, finance and urban management.

Revocable and Privacy-Preserving Decentralized Data Sharing Framework for Fog-Assisted Internet of Things

Fog-assisted Internet of Things (IoT) can outsource the massive data of resource-constraint IoT devices to cloud and fog nodes (FNs). Meanwhile, it enables convenient and low time-delay data-sharing services, which relies heavily on high security of data confidentiality and fine-grained access control. Many efforts have been focused on this urgent requirement by leveraging ciphertext-policy attribute-based encryption (CP-ABE). However, when deployed in fog-assisted IoT systems for secure data sharing, it remains a challenging problem of how to preserve attribute privacy of access policy, and trace-then-revoke traitors (i.e., malicious users intending to leak decryption keys for illegal profits) efficiently and securely in such a large scale and decentralized environment with resource-constraint user devices, especially in consideration of misbehaving cloud and FNs. Therefore, in this article, we propose a revocable and privacy-preserving decentralized data-sharing framework (RPDDSF) by designing a large universe and multiauthority CP-ABE scheme with fully hidden access policy for secure data sharing in IoT systems to achieve user attribute privacy preserving with unbounded attribute universe and key escrow resistance suitable for large scale and decentralized environment. Based on this, with RPDDSF, anyone can efficiently expose the traitors and punish them by forward/backward secure revocation. Besides, RPDDSF is able to guarantee data integrity for both data owners (DOs) and users to resist misbehaving cloud and FNs, alongwith low computation overhead for resource-constraint devices. Finally, RPDDSF is proven to be secure with detailed security proofs, and its high efficiency and feasibility are demonstrated by extensive performance evaluations.

A blockchain-based secure data-sharing framework for Software Defined Wireless Body Area Networks

In the past few years, blockchain has proven its necessity and unprecedented prospects in providing a secure environment for information exchange between two parties. The integration of the Internet of Things (IoT) with blockchain has enabled a digital transformation in many areas such as healthcare, supply chain, and financial services. Like blockchain, the programmable Software-Defined Network (SDN) is also achieving popularity due to its ability to reduce network management complexity. It is evident that incorporating SDNs with IoT-based healthcare systems can significantly improve healthcare management services. However, there are a number of challenges such as data confidentiality, user-centric design, integrity and privacy that makes it difficult to share sensitive information among different parties in a healthcare system. To overcome these challenges, we propose an architectural framework that incorporates blockchain with Software-Defined Wireless Body Area Networks (SDWBANs) to facilitate secure data sharing. We have also designed and integrated a smart contract based fine-grained access control policy to ensure that only data owners will have full control over their health data. The experimental outcomes show that the proposed model achieves good throughput and incurs a very low overhead in terms of latency compared to traditional cloud-based systems.