- Risk Assessment: Quantitative evaluation of re-identification risks using advanced metrics such as k-anonymity, l-diversity, and t-closeness.
- Tooling Expertise: Guided assistance with specialized open-source tools (primarily sdcMicro and ARX) for robust data transformation.
- Utility Preservation: Expert balancing of privacy protocols with the need to preserve the statistical and predictive utility of data for AI models.
Key Value: Ensuring strict GDPR compliance and identity protection while maintaining high analytical quality for scientific and computational use.
Practical example
Organizations must often share sensitive data for research while meeting rigorous privacy regulations.
This service facilitates, among other activities:
- Clinical Research Sharing: Assisting health authorities in anonymizing sensitive patient diagnoses and treatments for epidemiological studies with university partners.
- Socio-Demographic Transparency: Supporting municipalities in safely sharing resident data (age, household composition) for public research and intelligent dashboards.
- Risk Mitigation: Applying selective suppression and data aggregation in sparsely populated areas to eliminate concrete re-identification risks.
Case Study: The service enables a local health authority to share a robust clinical dataset for cancer research by defining optimal anonymization parameters that reduce risk without compromising the study’s scientific integrity.
