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Protecting personal health (PH) data is crucial in modern healthcare. This study reviews security and privacy challenges, legal frameworks, and advanced techniques like NoPeek learning and differential privacy for secure data analysis and AI applications.

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Area of Science:

  • Healthcare Informatics
  • Data Security and Privacy
  • Medical Data Management

Background:

  • Contemporary healthcare relies on diverse personal health (PH) data sources, including electronic health records (EHR), personal health records (PHR), wearables, and sensors.
  • Ensuring the security, privacy, and usability of PH data is paramount for improving patient treatments, enabling medical discoveries, and supporting governmental decision-making.
  • Key challenges in managing PH data include maintaining data confidentiality, standardization, accuracy, timeliness, consent management, and legal compliance.

Purpose of the Study:

  • To comprehensively review data security and privacy measures throughout the healthcare data life cycle.
  • To examine critical laws regulating healthcare data security and privacy.
  • To explore emerging privacy-preserving techniques and their application in healthcare data analysis and AI.

Main Methods:

  • Literature review of security and privacy challenges, legal frameworks, and solutions in healthcare data management.
  • Analysis of privacy-preserving techniques, including trusted execution environments and cryptographic methods.
  • Examination of advanced methods such as NoPeek learning and differential privacy for secure data analysis.

Main Results:

  • Privacy-preserving techniques are advancing, with significant developments in trusted execution environments and cryptography.
  • Best practices emphasize strict adherence to consent and privacy policies for utmost data care.
  • NoPeek learning and differential privacy offer methods for data analysis without compromising individual privacy.

Conclusions:

  • Integrating advanced privacy techniques with Artificial Intelligence (AI) is essential for secure and ethical healthcare advancements.
  • Balancing the benefits of AI in disease prediction with robust data protection and regulatory compliance is critical.
  • Collaborative approaches are needed to integrate predictive analytics into personalized healthcare while upholding trust and ethical standards.