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Toward Efficient Health Data Identification and Classification in IoMT-Based Systems.

Afnan Alsadhan1, Areej Alhogail1, Hessah A Alsalamah1

  • 1Information Systems Department, College of Computer and Information Science, King Saud University, Riyadh P.O. Box 145111, Saudi Arabia.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

The Internet of Medical Things (IoMT) framework SDAIPA classifies health data risks using HIPAA and SDAIA principles. This ensures better privacy protection and regulatory compliance for sensitive IoMT data.

Keywords:
HIPAA compliancedata governancedata identification and classification (DIC)health data privacyinternet of medical things (IoMT)medical data security

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Area of Science:

  • Medical Informatics
  • Cybersecurity
  • Health Data Management

Background:

  • The Internet of Medical Things (IoMT) enables personalized healthcare but poses significant privacy risks due to sensitive data exchange.
  • Effective Data Identification and Classification (DIC) is crucial for safeguarding IoMT data, ensuring regulatory compliance, and optimizing data management.

Purpose of the Study:

  • To introduce SDAIPA (SDAIA-HIPAA), a standardized hybrid framework for classifying IoMT health data.
  • To integrate HIPAA and SDAIA principles with a dual risk assessment (uniqueness and harm potential) for systematic IoMT data classification.

Main Methods:

  • Developed a hybrid classification framework (SDAIPA) integrating HIPAA and SDAIA principles.
  • Employed a dual risk perspective, considering data uniqueness and potential harm.
  • Validated the framework through expert domain review.

Main Results:

  • SDAIPA provides a structured, regulation-aligned process for classifying IoMT health data sensitivity.
  • The framework facilitates effective allocation of encryption resources, prioritizing high-risk data like genomic or location information.
  • Expert validation confirmed the framework's practicality for sensitivity-aware IoMT data management.

Conclusions:

  • SDAIPA offers a practical reference for managing IoMT data with a focus on privacy and regulatory alignment.
  • The framework supports healthcare providers, policymakers, and AI developers in enhancing data protection in smart healthcare systems.
  • SDAIPA promotes proportionate and regulation-aligned protection of sensitive health data within evolving IoMT ecosystems.