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Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC).

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Summary
This summary is machine-generated.

This study introduces an adaptive protocol using fully homomorphic encryption (FHE) for secure remote health monitoring (RHM). The system ensures patient data confidentiality and adaptive reporting for various health conditions, improving healthcare accessibility.

Keywords:
cloud computingfederated protocolfully-homomorphic encryptionheart diseasesremote healthcare (RHC)

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Area of Science:

  • Computer Science
  • Healthcare Technology
  • Cybersecurity

Background:

  • Remote health monitoring (RHM) improves healthcare access but faces security challenges, risking patient data privacy and HIPAA compliance.
  • Ensuring the confidentiality of sensitive medical data is paramount, especially with the increasing use of digital health records and remote services.

Purpose of the Study:

  • To design and implement an adaptive Autonomous Protocol (AutoPro) for remote healthcare (RHC) data using fully homomorphic encryption (FHE).
  • To perform adaptive, autonomous FHE computations on RHM data for secure health status reporting while maintaining patient confidentiality.
  • To develop a decentralized protocol operating independently among hospital servers without third-party reliance.

Main Methods:

  • The study utilizes an adaptive autonomous protocol (AutoPro) with fully homomorphic encryption (FHE) on patient remote healthcare (RHC) monitoring data.
  • An algorithm based on the fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant Gentry, Sahai, and Waters (GSW) scheme was employed.
  • A concrete-ML model was trained on a heart disease dataset, with preprocessing for data imputation, and evaluated on cloud infrastructure (AWS).

Main Results:

  • The FHE protocol achieved high accuracy with Support Vector Classification (SVC) at 88% and Linear Regression (LR) at 86%.
  • Area Under Curve (AUC) scores reached 91% for SVC and 90% for LR, demonstrating strong predictive performance.
  • The protocol was successfully implemented and demonstrated on the AWS cloud network, validating its performance in a real-world environment.

Conclusions:

  • The developed FHE-based protocol offers a novel, secure system for remote health monitoring, ensuring patient data confidentiality.
  • The adaptive nature of the protocol, based on patient condition severity, enhances its applicability across various RHC scenarios.
  • This system provides immediate emergency services and confidential patient reports, improving healthcare efficiency and patient trust.