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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 141–150 of 174 papers

TitleStatusHype
Performance of Automatic De-identification Across Different Note Types—0
Personalized and Invertible Face De-Identification by Disentangled Identity Information Manipulation—0
Enhancing Clinical Models with Pseudo Data for De-identificationCode0
De-identification of Privacy-related Entities in Job PostingsCode0
Automated Privacy-Preserving Techniques via Meta-LearningCode0
The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray GenerationCode0
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. PerformanceCode0
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language ModelsCode0
De-identification of Patient Notes with Recurrent Neural NetworksCode0
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistencyCode0
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