Ten quick tips for protecting health data using de-identification and perturbation of structured datasets

Area of Science:

• Health Informatics
• Data Privacy
• Biostatistics

Background:

• Structured patient data is shared for operational, research, and public health purposes.
• Protecting patient privacy and data confidentiality relies on de-identification and anonymization, but standards are lacking.
• Current anonymization techniques can be technically complex and lack universal acceptance.

Purpose of the Study:

• To provide practical recommendations for anonymizing structured health data.
• To support compliance with data protection laws and reduce re-identification risk.
• To promote responsible health data sharing for research and public health benefits.

Main Methods:

• Applying the principle of data minimization to avoid unnecessary data granularity.
• Implementing accessible strategies such as rounding, range replacement, jittering, and aggregation.
• Managing date values and separating sensitive data from identifying information to prevent linkage.

Main Results:

• Demonstrated practical methods for anonymizing structured health data.
• Provided guidance on reducing re-identification risks through various data perturbation techniques.
• Highlighted the importance of legal and governance frameworks in data anonymization.

Conclusions:

• Accessible anonymization strategies can be implemented without specialist expertise.
• Responsible health data sharing is achievable while upholding patient privacy and data utility.
• These guidelines support ethical and legal data stewardship in health research.