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Updated: Apr 10, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

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From theory to practice: Re-identification Challenge to test imaging data pseudonymization effectiveness.

Rocío Catalán Flores¹, Ignacio Gómez-Rico Junquero², Paula Jiménez Gómez²

¹Grupo de Investigación Biomédica en Imagen (GIBI230), Valencia, Spain. rocio_catalan@iislafe.es.

Scientific Data

|April 8, 2026

Summary

This summary is machine-generated.

Pseudonymization of DICOM images effectively protects patient privacy and ensures General Data Protection Regulation (GDPR) compliance. A challenge demonstrated that current pseudonymization profiles are robust, preventing patient re-identification.

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Area of Science:

Medical Imaging
Data Privacy
Cybersecurity

Background:

DICOM image pseudonymization is crucial for patient privacy and GDPR compliance.
Standardized methods for irreversible anonymization and proof of robustness are lacking.
Existing pseudonymization profiles carry data protection risks due to unassessed privacy threats.

Purpose of the Study:

To evaluate the effectiveness of pseudonymization profiles developed in European AI4HI projects (ChAImeleon, ProCancer-I).
To assess the robustness of these profiles against re-identification attempts.
To identify potential vulnerabilities in DICOM image pseudonymization.

Main Methods:

A re-identification challenge was conducted with 68 participants.
Participants attempted to re-identify 38 pseudonymized DICOM studies from diverse European sources.
Data were pseudonymized using ChAImeleon and ProCancer-I profiles.

Main Results:

No participants successfully re-identified any patient identities.
The study demonstrated the high effectiveness of the employed pseudonymization profiles.
Minor vulnerabilities were noted in free-text metadata and 3D reconstructions, but did not lead to re-identification.

Conclusions:

The pseudonymization profiles used are effective in preventing patient re-identification in DICOM images.
While minor vulnerabilities exist, current methods offer strong data protection.
Further research may focus on addressing identified minor vulnerabilities for enhanced security.