TrustNShare - Data Trust Model Balancing Privacy Risk, Reputation, and Incentives

Area of Science:

• Health Informatics
• Data Security
• Blockchain Technology

Background:

• Facilitating secure health data exchange is crucial for research and personalized medicine.
• Existing frameworks often struggle to balance data utility with robust privacy protection.
• Re-identification risks and lack of trust hinder effective health data sharing.

Purpose of the Study:

• To develop a novel framework, TrustNShare, for secure and privacy-preserving health data exchange.
• To integrate flexible privacy controls, reputation mechanisms, and incentive models.
• To mitigate data re-identification risks through blockchain-based logging.

Main Methods:

• Participatory development involving diverse stakeholders.
• Implementation of a blockchain-based system for tamper-proof logging.
• Development of a reputation model and incentive structures for data sharing.
• Creation of a data trust to inform negotiation processes.

Main Results:

• A functional framework enabling flexible privacy levels in health data exchange.
• Established reputation and incentive models validated through stakeholder engagement.
• Demonstrated mitigation of re-identification risks via secure logging.
• Enhanced data sharing negotiations informed by reputation and privacy risk data.

Conclusions:

• The TrustNShare framework effectively balances privacy, reputation, and incentives for health data exchange.
• Blockchain technology provides a secure and transparent foundation for health data management.
• Stakeholder involvement is key to developing trusted and usable data-sharing systems.