Precision health data: Requirements, challenges and existing techniques for data security and privacy

Area of Science:

• Health Informatics
• Data Security
• Personalized Medicine

Background:

• Precision health integrates diverse data (omics, lifestyle, medical records) for personalized care and illness prediction.
• Sensitive health data necessitates robust security, privacy, and trust to maintain public contribution and system effectiveness.
• Existing regulations and ethical guidelines mandate stringent data protection in healthcare.

Purpose of the Study:

• To explore global regulations, ethical guidelines, and domain-specific needs for precision health data.
• To investigate secure and privacy-preserving machine learning methods for precision health data computation.
• To present techniques and a conceptual model for secure and ethical precision health data utilization.

Main Methods:

• Review of international regulations and ethical frameworks.
• Investigation of secure and privacy-preserving machine learning algorithms.
• Development of a conceptual system model for data security and privacy.

Main Results:

• Identified key regulatory and ethical requirements for precision health data.
• Evaluated suitability of various secure machine learning techniques for health data.
• Proposed a conceptual model integrating compliance, consent, and innovation.

Conclusions:

• Ensuring data security, privacy, and trust is paramount for effective precision health.
• Secure machine learning methods are essential for processing sensitive health information.
• A comprehensive approach combining technical solutions and ethical governance is vital for advancing precision health.