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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 151–174 of 174 papers

TitleStatusHype
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning TasksCode0
DeIDClinic: A Multi-Layered Framework for De-identification of Clinical Free-text DataCode0
Medical Manifestation-Aware De-IdentificationCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
Pedestrian Attribute Editing for Gait Recognition and AnonymizationCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Generation and De-Identification of Indian Clinical Discharge Summaries using LLMsCode0
Natural Language Generation for Electronic Health RecordsCode0
Zero-shot generation of synthetic neurosurgical data with large language modelsCode0
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical RecordsCode0
A Privacy-Preserving Unsupervised Speaker Disentanglement Method for Depression Detection from SpeechCode0
Hide-and-Seek Privacy ChallengeCode0
Biomedical Named Entity Recognition at ScaleCode0
Improving speaker de-identification with functional data analysis of f0 trajectoriesCode0
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal MessagesCode0
In the Name of Fairness: Assessing the Bias in Clinical Record De-identificationCode0
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical DomainCode0
k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignmentCode0
RID-TWIN: An end-to-end pipeline for automatic face de-identification in videosCode0
The UU-Net: Reversible Face De-Identification for Visual Surveillance Video FootageCode0
PHICON: Improving Generalization of Clinical Text De-identification Models via Data AugmentationCode0
SVIA: A Street View Image Anonymization Framework for Self-Driving ApplicationsCode0
Privacy Guarantees for De-identifying Text TransformationsCode0
Automated Privacy-Preserving Techniques via Meta-LearningCode0
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