Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks

Area of Science:

• Biomedical Informatics
• Health Data Security
• Artificial Intelligence Ethics

Background:

• Increasing generation, sharing, and analysis of health data across diverse settings.
• Emerging privacy, security, and trust challenges beyond traditional frameworks.
• Need for robust strategies to manage risks in health data utilization.

Purpose of the Study:

• Characterize risks and protections throughout the health data life cycle.
• Discuss regulatory, organizational, and technological influences on data protection.
• Review emerging threats and mitigation strategies for health data.

Main Methods:

• Analysis of health data life cycle risks and protections.
• Review of technical approaches: access control, differential privacy, synthetic data generation.
• Exploration of collaborative mechanisms: federated learning, secure multiparty computation.

Main Results:

• Identified trade-offs between data privacy and utility.
• Highlighted practical challenges in scaling data protection methods.
• Discussed incidental disclosures as an emerging threat from AI tools.

Conclusions:

• Need for standardized metrics and transparency to build trust in data-driven healthcare and research.
• Importance of balancing data utility with robust privacy-preserving techniques.
• Ongoing challenges in implementing scalable and effective data protection strategies.