An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management
- URL: http://arxiv.org/abs/2410.03217v1
- Date: Fri, 4 Oct 2024 08:04:48 GMT
- Title: An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management
- Authors: Kishu Gupta, Deepika Saxena, Pooja Rani, Jitendra Kumar, Aaisha Makkar, Ashutosh Kumar Singh, Chung-Nan Lee,
- Abstract summary: 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.
- Score: 4.828148213747833
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. 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.
Related papers
- Securing The Future Of Healthcare: Building A Resilient Defense System For Patient Data Protection [0.0]
The study predicts the severity of healthcare data breaches using a gradientboosting machine learning model.
The findings revealed that hacking and IT incidents are the most common type of breaches in the healthcare industry.
The model evaluation showed that the gradient boosting algorithm performs well.
arXiv Detail & Related papers (2024-07-23T04:25:35Z) - Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems [1.8434042562191815]
The Internet of Medical Things (IoMT) transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive prevention.
Its benefits are countered by significant security challenges that endanger the lives of its users due to the sensitivity and value of the processed data.
A new framework for Intrusion Detection Systems (IDS) is introduced, leveraging Artificial Neural Networks (ANN) for intrusion detection while utilizing Federated Learning (FL) for privacy preservation.
arXiv Detail & Related papers (2024-03-14T11:57:26Z) - 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) - HNMblock: Blockchain technology powered Healthcare Network Model for epidemiological monitoring, medical systems security, and wellness [6.2997667081978825]
This paper introduces HNMblock, a model that elevates the realms of epidemiological monitoring, medical system security, and wellness enhancement.
By harnessing the transparency and immutability inherent in blockchain, HNMblock empowers real-time, tamper-proof tracking of epidemiological data.
It fortifies the security of medical systems through advanced cryptographic techniques and smart contracts, with a paramount focus on safeguarding patient privacy.
arXiv Detail & Related papers (2024-02-10T21:57:22Z) - The Evolution of Quantum Secure Direct Communication: On the Road to the
Qinternet [49.8449750761258]
Quantum secure direct communication (QSDC) is provably secure and overcomes the threat of quantum computing.
We will detail the associated point-to-point communication protocols and show how information is protected and transmitted.
arXiv Detail & Related papers (2023-11-23T12:40:47Z) - Blockchain-empowered Federated Learning for Healthcare Metaverses:
User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses.
We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
arXiv Detail & Related papers (2023-07-29T12:54:03Z) - When Quantum Information Technologies Meet Blockchain in Web 3.0 [86.91054991998273]
We introduce a quantum blockchain-driven Web 3.0 framework that provides information-theoretic security for decentralized data transferring and payment transactions.
We discuss the potential applications and challenges of implementing quantum blockchain in Web 3.0.
arXiv Detail & Related papers (2022-11-29T05:38:42Z) - User-Centric Health Data Using Self-sovereign Identities [69.50862982117127]
This article presents the potential use of the issuers Self-Sovereign Identities (SSI) and Distributed Ledger Technologies (DLT) to improve the privacy and control of health data.
The paper lists the prominent use cases of decentralized identities in the health area, and discusses an effective blockchain-based architecture.
arXiv Detail & Related papers (2021-07-26T17:09:52Z) - A Review-based Taxonomy for Secure Health Care Monitoring: Wireless
Smart Cameras [9.4545147165828]
This research focuses on the secure storage of patient and medical records in the healthcare sector.
A potential solution comes from biometrics, although their use may be time-consuming and can slow down data retrieval.
This research aims to overcome these challenges and enhance data access control in the healthcare sector through the addition of biometrics in the form of fingerprints.
arXiv Detail & Related papers (2021-07-05T11:59:10Z) - 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) - Privacy-preserving medical image analysis [53.4844489668116]
We present PriMIA, a software framework designed for privacy-preserving machine learning (PPML) in medical imaging.
We show significantly better classification performance of a securely aggregated federated learning model compared to human experts on unseen datasets.
We empirically evaluate the framework's security against a gradient-based model inversion attack.
arXiv Detail & Related papers (2020-12-10T13:56:00Z)
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.