Towards an evolution in the characterization of the risk of re-identification of medical images.

As facial recognition technology proliferates, concerns emerge regarding its application to medical imaging, specifically Magnetic Resonance Imaging (MRI). This paper investigates privacy risks associated with MRI data, including re-identification through social network photographs and sensitive attribute inference. The exponential growth in MRI quality coincides with the increasing sophistication of facial recognition tools, raising the potential for re-identification using medical images. Our attack involves reconstructing faces and applying facial recognition techniques to extract identifying features that can be compared to photographs. Legal frameworks like GDPR mandate the assessment and protection of personal data, necessitating continuous risk evaluation. Beyond re-identification, we explore the inference of individual attributes from MRI images, such as age, gender, and ethnic group. This research assesses the privacy risks associated with MRI data by taking into account the evolution of facial recognition and reconstruction tools that have become increasingly accessible. We also show that facial hair removal technique on photographs increases the risk of re-identification. Overall, our results highlight vulnerabilities in sharing MRI data, emphasizing the need for enhanced privacy safeguards.