Layered Privacy Language Pseudonymization Extension for Health Care

Area of Science:

• Health Informatics
• Data Privacy Law
• Computer Science

Background:

• The General Data Protection Regulation (GDPR) mandates stringent data privacy, particularly for sensitive health data.
• Existing privacy frameworks require robust methods for de-identifying health information.
• Layered Privacy Language (LPL) offers a model for enforcing privacy policies.

Purpose of the Study:

• To enhance privacy policy modeling for health data protection under GDPR.
• To integrate pseudonymization techniques into the LPL framework.
• To demonstrate the effectiveness of policy-based de-identification with pseudonymization in healthcare.

Main Methods:

• An overview of various pseudonymization methods was conducted.
• A pseudonymization extension was developed for the Layered Privacy Language.
• The extension was integrated into the Policy-based De-identification process within LPL.
• A healthcare scenario was used to configure and demonstrate the policy.

Main Results:

• Pseudonymization capabilities are crucial for healthcare data privacy.
• The LPL framework was successfully extended to support diverse pseudonymization methods.
• Policy-based de-identification in LPL can effectively incorporate pseudonymization for health data.
• Demonstrated a practical application of the extended LPL in a healthcare context.

Conclusions:

• The developed pseudonymization extension significantly enhances the LPL's capability for health data de-identification.
• This approach strengthens GDPR compliance for sensitive health information.
• The integration of pseudonymization into policy-based de-identification provides a flexible and effective privacy solution for healthcare.