Dwarna : a blockchain solution for dynamic consent in biobanking

Dynamic consent aims to empower research partners and facilitate active participation in the research process. Used within the context of biobanking, it gives individuals access to information and control to determine how and where their biospecimens and data should be used. We present Dwarna—a web portal for ‘dynamic consent’ that acts as a hub connecting the different stakeholders of the Malta Biobank: biobank managers, researchers, research partners, and the general public. The portal stores research partners’ consent in a blockchain to create an immutable audit trail of research partners’ consent changes. Dwarna’s structure also presents a solution to the European Union’s General Data Protection Regulation’s right to erasure—a right that is seemingly incompatible with the blockchain model. Dwarna’s transparent structure increases trustworthiness in the biobanking process by giving research partners more control over which research studies they participate in, by facilitating the withdrawal of consent and by making it possible to request that the biospecimen and associated data are destroyed.peer-reviewe

Achieving cybersecurity in blockchain-based systems: a survey

With The Increase In Connectivity, The Popularization Of Cloud Services, And The Rise Of The Internet Of Things (Iot), Decentralized Approaches For Trust Management Are Gaining Momentum. Since Blockchain Technologies Provide A Distributed Ledger, They Are Receiving Massive Attention From The Research Community In Different Application Fields. However, This Technology Does Not Provide With Cybersecurity By Itself. Thus, This Survey Aims To Provide With A Comprehensive Review Of Techniques And Elements That Have Been Proposed To Achieve Cybersecurity In Blockchain-Based Systems. The Analysis Is Intended To Target Area Researchers, Cybersecurity Specialists And Blockchain Developers. For This Purpose, We Analyze 272 Papers From 2013 To 2020 And 128 Industrial Applications. We Summarize The Lessons Learned And Identify Several Matters To Foster Further Research In This AreaThis work has been partially funded by MINECO, Spain grantsTIN2016-79095-C2-2-R (SMOG-DEV) and PID2019-111429RB-C21 (ODIO-COW); by CAM, Spain grants S2013/ICE-3095 (CIBERDINE),P2018/TCS-4566 (CYNAMON), co-funded by European Structural Funds (ESF and FEDER); by UC3M-CAM grant CAVTIONS-CM-UC3M; by the Excellence Program for University Researchers, Spain; and by Consejo Superior de Investigaciones Científicas (CSIC), Spain under the project LINKA20216 (“Advancing in cybersecurity technologies”, i-LINK+ program)

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

Blockchain for Healthcare Systems: Concepts, Applications, Challenges, and Future Trends

-Electronic medical records are digital documents that contain medical data pertaining to a patient\u27s medical care. Because electronic health records are regularly exchanged amongst stakeholders in healthcare, they are prone to a range of challenges such as data misuse and loss of privacy and security. These challenges may be solved by utilizing blockchain-based technologies in the healthcare area. Blockchain is a decentralized innovative technology that can completely transform, reshape, and reinvent how data is stored and processed in the healthcare sector. In this article, we offer an overview of the blockchain, its formation, its types, and how it works. We review the various applications of blockchain in the medical field and how Blockchain revolutionized the medical industry. We highlight previous scientific research on the application of blockchain to electronic health record systems (EHRs). Finally, we discuss the open research problems that limit the use of blockchain in the medical field