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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 31–40 of 174 papers

TitleStatusHype
Improving speaker de-identification with functional data analysis of f0 trajectoriesCode0
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. PerformanceCode0
PHICON: Improving Generalization of Clinical Text De-identification Models via Data AugmentationCode0
Automated Privacy-Preserving Techniques via Meta-LearningCode0
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning TasksCode0
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistencyCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical DomainCode0
De-identification of Patient Notes with Recurrent Neural NetworksCode0
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