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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 51–75 of 174 papers

TitleStatusHype
De-Identification of Emails: Pseudonymizing Privacy-Sensitive Data in a German Email Corpus—0
De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks—0
Audio De-identification: A New Entity Recognition Task—0
De-identification of medical records using conditional random fields and long short-term memory networks—0
Applying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records—0
AnonymousNet: Natural Face De-Identification with Measurable Privacy—0
De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective—0
De-identification without losing faces—0
De-identifying Free Text of Japanese Dummy Electronic Health Records—0
De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models—0
Privacy Protection in MRI Scans Using 3D Masked Autoencoders—0
Creating and Evaluating a Synthetic Norwegian Clinical Corpus for De-Identification—0
Conditional De-Identification of 3D Magnetic Resonance Images—0
A Systematical Solution for Face De-identification—0
A survey of automatic de-identification of longitudinal clinical narratives—0
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data—0
Divide and Conquer: a Two-Step Method for High Quality Face De-identification with Model Explainability—0
Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels—0
Disguise without Disruption: Utility-Preserving Face De-Identification—0
CodE Alltag 2.0 --- A Pseudonymized German-Language Email Corpus—0
DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization—0
Digital Speech Algorithms for Speaker De-Identification—0
Dutch Named Entity Recognition and De-identification Methods for the Human Resource Domain—0
EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model—0
AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework—0
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