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Background: Tuberculosis (TB) is an infectious disease and is among the top 10 causes of death in the world, and Brazil is part of the top 30 high TB burden countries. Data collection is an essential practice in health studies, and the adoption of electronic data capture (EDC) systems can positively increase the experience of data acquisition and analysis. Also, data-sharing capabilities are crucial to the construction of efficient and effective evidence-based decision-making tools for managerial and operational actions in TB services. Data must be held secure and traceable, as well as available and understandable, for authorized parties.
Objectives: In this sense, this work aims to propose a blockchain-based approach to build a reusable, decentralized, and de-identified dataset of TB research data, while increasing transparency, accountability, availability, and integrity of raw data collected in EDC systems.
Methods: After identifying challenges and gaps, a solution was proposed to tackle them, considering its relevance for TB studies. Data security issues are being addressed by a blockchain network and a lightweight and practical governance model. Research Electronic Data Capture (REDCap) and KoBoToolbox are used as EDC systems in TB research. Mechanisms to de-identify data and aggregate semantics to data are also available.
Results: A permissioned blockchain network was built using Kaleido platform. An integration engine integrates the EDC systems with the blockchain network, performing de-identification and aggregating meaning to data. A governance model addresses operational and legal issues for the proper use of data. Finally, a management system facilitates the handling of necessary metadata, and additional applications are available to explore the blockchain and export data.
Conclusions: Research data are an important asset not only for the research where it was generated, but also to underpin studies replication and support further investigations. The proposed solution allows the delivery of de-identified databases built in real time by storing data in transactions of a permissioned network, including semantic annotations, as data are being collected in TB research. The governance model promotes the correct use of the solution.
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http://dx.doi.org/10.1055/s-0041-1727194 | DOI Listing |
J Vis Exp
August 2025
School of Cyberspace Security, Zhengzhou University.
In the context of the rapid development of large language models (LLMs), contrastive learning has become widely adopted due to its ability to bypass costly data annotation by leveraging vast amounts of network data for model training. However, this widespread use raises significant concerns regarding data privacy protection. Unlearnable Examples (UEs), a technique that disrupts model learning by perturbing data, effectively prevents unauthorized models from misusing sensitive data.
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August 2025
School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
Introduction: Digital content, including images and videos, is increasingly ruling the online world, and so multimedia services form a part of this modern life. However, the digital resources face significant problems, especially regarding copyright infringement. In such an instance, any modification without authority infringes intellectual property rights.
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September 2025
Department of CSE, Ahsanullah University of Science and Technology (AUST), 141 & 142, Love Road, Tejgaon Industrial Area, Dhaka, 1208, Bangladesh.
Modern immigration management systems face three critical challenges: security vulnerabilities in centralized databases exemplified by a 2023 breach exposing over 120,000 records processing inefficiencies resulting in delays over 30 days, and interoperability gaps among stakeholders. Although blockchain has been proposed, current implementations often sacrifice regulatory compliance (e.g.
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December 2025
Department of Computer Science and Engineering, GITAM Deemed to be University, Andhra Pradesh, India.
Cervical cancer is a serious health concern that entails high risks for individuals due to delayed detection and treatment worldwide. Formal screening for the condition is challenging in developing countries due to several factors, including medical costs, access to healthcare facilities, and delayed symptom manifestation. A blockchain-enabled healthcare system for cervical cancer risk prediction ensures data security, privacy, and accurate risk assessment.
View Article and Find Full Text PDFSensors (Basel)
August 2025
College of Computing and Intelligent Systems, University of Khorfakkan, Sharjah 18119, United Arab Emirates.
Software-Defined Wide-Area Networks (SD-WAN) efficiently manage and route traffic across multiple WAN connections, enhancing the reliability of modern enterprise networks. However, the performance of SD-WANs is largely affected due to malicious activities of unauthorized and faulty nodes. To solve these issues, many machine-learning-based malicious-node-detection techniques have been proposed.
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