The recently published research paper “Estimating the Success of Re-identifications in Incomplete Datasets Using Generative Models” shows how the likelihood of a specific individual to have been correctly re-identified can be estimated with high accuracy even when an anonymized dataset is heavily incomplete. The presented results reject the claims that, first, re-identification is not a practical risk and, second, sampling or releasing partial datasets provide plausible deniability. Moving forward, the results also question whether current de-identification practices satisfy the anonymization standards of modern data protection laws such as GDPR and CCPA and emphasize the need to move, from a legal and regulatory perspective, beyond the de-identification release-and-forget model.
