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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 121–130 of 174 papers

TitleStatusHype
GiusBERTo: A Legal Language Model for Personal Data De-identification in Italian Court of Auditors Decisions—0
HB Deid - HB De-identification tool demonstrator—0
Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification—0
IdentityDP: Differential Private Identification Protection for Face Images—0
Medical records condensation: a roadmap towards healthcare data democratisation—0
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training—0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
Language Resources to Support Language Diversity – the ELRA Achievements—0
Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes—0
Large Language Models for Patient Comments Multi-Label Classification—0
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