An anonymization-based privacy-preserving data collection protocol for digital health data.

Area of Science:

• Health Informatics
• Data Privacy
• Computer Science

Background:

• Digital health data collection is crucial for healthcare and research but poses significant privacy challenges.
• Existing privacy-preserving methods often rely on third parties or private channels, limiting their applicability and security.
• These methods are susceptible to privacy attacks, necessitating more robust solutions.

Purpose of the Study:

• To propose a novel privacy-preserving data collection protocol for healthcare.
• To anonymize sensitive health data without relying on third-party anonymizers or private channels.
• To address limitations of existing methods and enhance data security in digital health.

Main Methods:

• Implemented a clustering-based k-anonymity model to prevent identity disclosure.
• Restricted communication to elected representatives from equivalent data owner groups.
• Developed solutions for leader collusion and sensitive attribute protection.
• Utilized a greedy heuristic method for dynamic data owner management.

Main Results:

• The proposed protocol effectively anonymizes healthcare data while preventing identity disclosure.
• The method addresses the identified 'leader collusion' privacy attack.
• Dynamic data owner management is efficiently handled.
• Experimental results on real-world datasets demonstrate superior privacy protection and computational efficiency compared to state-of-the-art techniques.

Conclusions:

• The novel protocol offers a secure and efficient solution for privacy-preserving digital health data collection.
• It overcomes the limitations of existing methods by eliminating third-party reliance.
• The approach enhances data security for healthcare research, paving the way for more trustworthy data utilization.