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. 2022 Sep 15;28(9):1883–1892. doi: 10.1038/s41591-022-01966-1

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 6 — a, Study workflow. The re-identification algorithms were used to find the ID of the patient from a database of 405 patients when given the original image, a cropped image or the DM-reconstructed image of a patient as a query image. b, Performance of the three re-identification algorithms tested, as assessed by TAR@FAR = 0.1, TAR@FAR = 0.01, Rank-1 (left) and ROC curves (right). The re-identification algorithms were trained on the CASIA-Webface dataset. TAR@FAR = X indicates the TAR when the FAR equals X. Rank-1 is the probability that the similarity score of the same identity ranks first among all the identities. Lower values of TAR@FAR = 0.1, TAR@FAR = 0.01 and Rank-1 indicate weaker performance of the re-identification algorithm and better performance of the privacy protection technology. TAR = TP/(TP + FN); FAR = FP/(FP + TN). TP, true positive; FP, false positive; TN, true negative; FN, false negative.