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De-identification

De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.

Papers

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Showing 21–30 of 174 papers

TitleStatusHype
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4Code1
A Privacy-Preserving Unsupervised Speaker Disentanglement Method for Depression Detection from SpeechCode0
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning TasksCode0
Pedestrian Attribute Editing for Gait Recognition and AnonymizationCode0
Enhancing Clinical Models with Pseudo Data for De-identificationCode0
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistencyCode0
Biomedical Named Entity Recognition at ScaleCode0
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical RecordsCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Automated Privacy-Preserving Techniques via Meta-LearningCode0
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