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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

Title	Date	Tasks	Status	Hype	Score
Hide-and-Seek Privacy Challenge	Jul 23, 2020	De-identificationSynthetic Data Generation	CodeCode Available	0	5
k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment	Mar 20, 2023	De-identificationGenerative Adversarial Network	CodeCode Available	0	5
Natural Language Generation for Electronic Health Records	Jun 1, 2018	DecoderDe-identification	CodeCode Available	0	5
Automated Privacy-Preserving Techniques via Meta-Learning	Jun 24, 2024	De-identificationMeta-Learning	CodeCode Available	0	5
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks	Aug 2, 2019	De-identificationLength-of-Stay prediction	CodeCode Available	0	5
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency	May 26, 2023	Brain MorphometryDe-identification	CodeCode Available	0	5
Pedestrian Attribute Editing for Gait Recognition and Anonymization	Mar 9, 2023	AttributeData Augmentation	CodeCode Available	0	5
Computational Job Market Analysis with Natural Language Processing	Apr 29, 2024	Active LearningDe-identification	CodeCode Available	0	5
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain	May 19, 2020	De-identification	CodeCode Available	0	5
De-identification of Patient Notes with Recurrent Neural Networks	Jun 10, 2016	De-identificationFeature Engineering	CodeCode Available	0	5

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