Lensing Legal Dynamics for Examining Responsibility and Deliberation of Generative AI-Tethered Technological Privacy Concerns

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 146 - 167

Published: April 4, 2024

The rapid integration of generative AI technology across various domains has brought forth a complex interplay between technological advancements, legal frameworks, and ethical considerations. In world where transcended its initial novelty is now woven into the fabric everyday life, boundaries human creativity machine-generated output are becoming increasingly blurred. paper scrutinizes existing privacy laws regulations through lens AI, seeking to uncover gaps, challenges, possible avenues for reform. It explores evolution jurisprudence in face disruption debates adequacy current frameworks address dynamic complexities AI-influenced infringements. By scrutinizing cases personal data been exploited by nefarious actors employing malevolent purposes, stark reality emerges: emergence new avenue breaches that tests limits frameworks.

Language: Английский

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Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions

IEEE Internet of Things Journal, Journal Year: 2023, Volume and Issue: 10(24), P. 21873 - 21891

Published: Aug. 14, 2023

Recent technological advancements have considerably improved healthcare systems to provide various intelligent services, improving life quality. The Metaverse, often described as the next evolution of Internet, helps users interact with each other and environment, thus offering a seamless connection between virtual physical worlds. Additionally, by integrating emerging technologies, such artificial intelligence (AI), cloud edge computing, Internet Things (IoT), blockchain, semantic communications, can potentially transform many vertical domains in general sector (healthcare Metaverse) particular. Metaverse holds huge potential revolutionize development systems, presenting new opportunities for significant delivery, personalized experiences, medical education, collaborative research, so on. However, challenges are associated realization privacy, interoperability, data management, security. Federated learning (FL), branch AI, opens up enormous deal aforementioned exploiting computing resources available at distributed devices. This motivated us present survey on adopting FL Metaverse. Initially, we preliminaries IoT-based conventional healthcare, Furthermore, benefits discussed. Subsequently, discuss several applications FL-enabled including diagnosis, patient monitoring, infectious disease, drug discovery. Finally, highlight solutions toward realizing

Language: Английский

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Federated Learning Meets Blockchain in Decentralized Data Sharing: Healthcare Use Case

IEEE Internet of Things Journal, Journal Year: 2024, Volume and Issue: 11(11), P. 19602 - 19615

Published: Feb. 19, 2024

In the era of data-driven healthcare, amalgamation blockchain and Federated Learning (FL) introduces a paradigm shift towards secure, collaborative, patient-centric data-sharing. This paper pioneers exploration conceptual framework technical synergy FL for decentralized data-sharing, aiming to strike balance between data utility privacy. FL, machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data. Combined with blockchain, transparent immutable ledger, it establishes an ecosystem fostering trust, security, integrity. The elucidates foundations unravelling their roles in reshaping vividly illustrates potential impact this fusion on care. proposed approach preserves privacy while granting providers researchers access diversified datasets, ultimately leading more accurate models improved diagnoses. findings underscore acceleration medical research, treatment outcomes, empowerment through ownership. envisions that prioritizes individual propels advancements science.

Language: Английский

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Privacy-preserving in Blockchain-based Federated Learning systems

Computer Communications, Journal Year: 2024, Volume and Issue: 222, P. 38 - 67

Published: April 21, 2024

Language: Английский

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Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 130860 - 130887

Published: Jan. 1, 2023

The combination of drones and Intelligent Reflecting Surfaces (IRS) have emerged as potential technologies for improving the performance six Generation (6G) communication networks by proactively modifying wireless through smart signal reflection manoeuvre control. By deploying IRS on drones, it becomes possible to improve coverage reliability network while reducing energy consumption costs. Furthermore, integrating with Federated Learning (FL) can further boost drone enabling collaborative learning among multiple leading better more efficient decision-making holding great promise 6G networks. Therefore, we present a novel framework FL meets in 6G. In this framework, IRS-equipped swarm are deployed form distributed network, where techniques used collaborate process optimize coefficients each drone-IRS. This allows adapt changing environments quality services. Integrating into offers several advantages over traditional networks, including rapid deployment emergencies or disasters, improved services, increased accessibility remote areas. Finally, highlight challenges opportunities researchers interested We also help drive innovation developing

Language: Английский

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A critical literature review of security and privacy in smart home healthcare schemes adopting IoT & blockchain: Problems, challenges and solutions

Blockchain Research and Applications, Journal Year: 2023, Volume and Issue: 5(2), P. 100178 - 100178

Published: Dec. 20, 2023

Protecting private data in smart homes, a popular Internet-of-Things (IoT) application, remains significant security and privacy challenge due to the large-scale development distributed nature of IoT networks. Recently, healthcare has leveraged home systems, thereby compounding concerns terms confidentiality sensitive by extension owner. However, PoA-based Blockchain DLT emerged as promising solution for protecting from indiscriminate use preserving individuals residing IoT-enabled homes. This review elicits some concerns, issues, problems that have hindered adoption blockchain (BCoT) domains suggests requisite solutions using aging-in-place scenario. Implementation issues with BCoT were examined well combined challenges can pose when utilised gains. The study discusses recent findings, opportunities, barriers, provide recommendations could facilitate continuous growth application healthcare. Lastly, then explored potential permission an applicable consent-based model decision-making information disclosure process, including publisher-subscriber contracts fine-grained access control ensure secure processing sharing, ethical trust personal disclosure, direction. proposed authorisation framework guarantee ownership, conditional management, scalable tamper-proof storage, more resilient system against threat models such interception insider attacks.

Language: Английский

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A survey on federated learning for security and privacy in healthcare applications

Computer Communications, Journal Year: 2023, Volume and Issue: 207, P. 113 - 127

Published: May 19, 2023

Language: Английский

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Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(9)

Published: Aug. 8, 2024

Abstract The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare, addressing critical challenges securing electronic health records (EHRs), ensuring data privacy, facilitating secure transmission. This study provides comprehensive analysis the adoption AI within spotlighting their role fortifying security transparency leading trajectory for promising future realm healthcare. Our study, employing PRISMA model, scrutinized 402 relevant articles, narrative to explore review includes architecture blockchain, examines applications with without integration, elucidates interdependency between blockchain. major findings include: (i) it protects transfer, digital records, security; (ii) enhances EHR COVID-19 transmission, thereby bolstering healthcare efficiency reliability through precise assessment metrics; (iii) addresses like security, decentralized computing, forming robust tripod. revolutionize by EHRs, enhancing security. Private reflects sector’s commitment improved accessibility. convergence promises enhanced disease identification, response, overall efficacy, key sector challenges. Further exploration advanced features integrated enhance outcomes, shaping global delivery guaranteed innovation.

Language: Английский

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BDLT-IoMT—a novel architecture: SVM machine learning for robust and secure data processing in Internet of Medical Things with blockchain cybersecurity

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 81(1)

Published: Dec. 10, 2024

The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number recent rapid fluctuations. These changes have primarily affected the areas management, end-to-end device interconnectivity, resource organization, communication, networking, application-related aspects ICT. Owing complex structure applicational connectedness, evaluating each aforementioned opportunities concurrently reflects idea heterogeneity. association multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, massive volume everyday media data generated modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical optimization, intelligent network activities organization are necessary. Blockchain plays crucial role providing distributed storage sharing, exchange for automated decision-making, privacy, security AI-enabled machine learning (ML) models. However, models—support vector machine, particular—have impact on growth consortium networks among connected nodes, resolving issues with scalability, processing. By three main problems seamless peer-to-peer between infrastructure we novel technique this proposed architecture. approach is unique, as demonstrated by simulation-based results, which display huge differences 1.37%, 1.56%, 1.87%, respectively. background evaluation consists following areas: (i) protect decision-making; (ii) integrity smooth sharing exchange; (iii) optimization enable across heterogeneous devices.

Language: Английский

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CE-PBFT: A high availability consensus algorithm for large-scale consortium blockchain

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(2), P. 101957 - 101957

Published: Feb. 1, 2024

The consortium blockchain has been widely applied in various fields such as agricultural product traceability, supply chain management, and logistics transportation. As an indispensable component of a blockchain, the consensus algorithm ensures consistency trustworthiness each node network. However, existing algorithms large-scale scenarios suffer from low system throughput high latency due to complexity communication processes, rendering them impractical for real-world use. To address these issues, this paper proposes novel called credit evaluation-based practical Byzantine fault tolerance (CE-PBFT). This designs new evaluation model that considers completion rate, decay, behavior. It effectively measures reflects specific reliability status nodes during operation, thereby enhancing security. Additionally, introduces innovative use decision tree analyze network behavior simplifies protocol. Nodes are categorized excellent, good, ordinary, or poor based on classification results, non-Byzantine dynamically selected accordingly. greatly improves overall efficiency system. performance CE-PBFT is validated through experiments compared with PBFT, G-PBFT, RBFT, WBFT PPoR. Experimental results demonstrate scenarios, significantly throughput, reduces transaction overhead, outperforms protocols.

Language: Английский

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