Adaptive Security in 6G for Sustainable Healthcare
- URL: http://arxiv.org/abs/2403.01100v1
- Date: Sat, 2 Mar 2024 05:48:52 GMT
- Title: Adaptive Security in 6G for Sustainable Healthcare
- Authors: Ijaz Ahmad, Ijaz Ahmad, Erkki Harjula,
- Abstract summary: 6G will fulfill the requirements of future digital healthcare systems through emerging decentralized computing and secure communications technologies.
Digital healthcare solutions employ numerous low-power and resource-constrained connected things, such as the Internet of Medical Things (IoMT)
- Score: 1.4747234049753455
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 6G will fulfill the requirements of future digital healthcare systems through emerging decentralized computing and secure communications technologies. Digital healthcare solutions employ numerous low-power and resource-constrained connected things, such as the Internet of Medical Things (IoMT). However, the current digital healthcare solutions will face two major challenges. First, the proposed solutions are based on the traditional IoT-Cloud model that will experience latency and reliability challenges to meet the expectations and requirements of digital healthcare, while potentially inflicting heavy network load. Second, the existing digital healthcare solutions will face security challenges due to the inherent limitations of IoMT caused by the lack of resources for proper security in those devices. Therefore, in this research, we present a decentralized adaptive security architecture for the successful deployment of digital healthcare. The proposed architecture leverages the edge-cloud continuum to meet the performance, efficiency, and reliability requirements. It can adapt the security solution at run-time to meet the limited capacity of IoMT devices without compromising the security of critical data. Finally, the research outlines comprehensive methodologies for validating the proposed security architecture.
Related papers
- An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management [4.828148213747833]
This paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management.
The proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning.
The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security.
arXiv Detail & Related papers (2024-10-04T08:04:48Z) - Adaptive Lightweight Security for Performance Efficiency in Critical Healthcare Monitoring [1.1874952582465603]
The Internet of Things (IoT) with its diverse technologies has become an integral component of future healthcare systems.
The evolving healthcare paradigm requires adaptive security procedures and technologies that can adapt to the varying resource constraints of IoT devices.
This article brings forth the unique healthcare monitoring requirements and studies the existing encryption-based security approaches to provide the necessary security.
arXiv Detail & Related papers (2024-06-06T06:55:16Z) - A Scalable Multi-Layered Blockchain Architecture for Enhanced EHR Sharing and Drug Supply Chain Management [3.149883354098941]
This article presents an innovative Electronic Health Records (EHR) sharing and drug supply chain management framework.
The framework introduces five layers and transactions, prioritizing patient-centric healthcare by granting patients comprehensive access control over their health information.
It provides transparency and real-time drug supply monitoring, empowering decision-makers with actionable insights.
arXiv Detail & Related papers (2024-02-27T09:20:16Z) - Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content.
In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks.
We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
arXiv Detail & Related papers (2024-02-21T06:22:41Z) - Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare.
The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body.
HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed.
Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
arXiv Detail & Related papers (2024-01-22T03:17:41Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - Multi-Tier Computing-Enabled Digital Twin in 6G Networks [50.236861239246835]
In Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development.
The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns.
To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing.
arXiv Detail & Related papers (2023-12-28T13:02:53Z) - Challenges of Blockchain Applications in Digital Health: A Systematic
Review [0.0]
This systematic literature review aims to explore the challenges of blockchain applications in digital health.
Key issues identified include regulatory compliance, energy consumption, network effects, data standards, and the accessibility of the technology to stakeholders.
arXiv Detail & Related papers (2023-04-08T20:50:20Z) - Federated Learning and Blockchain-enabled Fog-IoT Platform for Wearables
in Predictive Healthcare [6.045977607688583]
We propose a platform using federated learning and private blockchain technology within a fog-IoT network.
These technologies have privacy-preserving features securing data within the network.
According to experimental results, the introduced implementation can effectively preserve a patient's privacy and a predictive service's integrity.
arXiv Detail & Related papers (2023-01-11T15:16:44Z) - AI-Empowered Data Offloading in MEC-Enabled IoV Networks [40.75165195026413]
This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit.
Various challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network.
arXiv Detail & Related papers (2022-03-31T09:31:53Z) - Edge Intelligence for Empowering IoT-based Healthcare Systems [42.909808437026136]
This article highlights the benefits of edge intelligent technology, along with AI in smart healthcare systems.
A novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems.
arXiv Detail & Related papers (2021-03-22T19:35:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.