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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 76–100 of 174 papers

TitleStatusHype
Toward Face Biometric De-identification using Adversarial Examples—0
Divide and Conquer: a Two-Step Method for High Quality Face De-identification with Model Explainability—0
StyleGAN as a Utility-Preserving Face De-identification Method—0
Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples—0
An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents—0
De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks—0
Open video data sharing in developmental and behavioural science—0
Towards Privacy-Preserving Person Re-identification via Person Identify Shift—0
End-to-end speech recognition modeling from de-identified data—0
The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models—0
Cross-Clinic De-Identification of Swedish Electronic Health Records: Nuances and Caveats—0
Spanish Datasets for Sensitive Entity Detection in the Legal Domain—0
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data—0
MAPA Project: Ready-to-Go Open-Source Datasets and Deep Learning Technology to Remove Identifying Information from Text Documents—0
Language Resources to Support Language Diversity – the ELRA Achievements—0
DeepDefacer: Automatic Removal of Facial Features via U-Net Image Segmentation—0
Utility Preservation of Clinical Text After De-Identification—0
Improving speaker de-identification with functional data analysis of f0 trajectoriesCode0
A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data—0
Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing—0
Speaker Identification Experiments Under Gender De-Identification—0
Digital Speech Algorithms for Speaker De-Identification—0
Towards a Data Privacy-Predictive Performance Trade-off—0
Conditional De-Identification of 3D Magnetic Resonance Images—0
Named Entity Recognition in Unstructured Medical Text Documents—0
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