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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
Data-Constrained Synthesis of Training Data for De-Identification—0
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis—0
DeepDefacer: Automatic Removal of Facial Features via U-Net Image Segmentation—0
Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation—0
Deep Learning Architecture for Patient Data De-identification in Clinical Records—0
De-identification In practice—0
De-identification is not always enough—0
De-Identification of Clinical Free Text in Dutch with Limited Training Data: A Case Study—0
De-identification of clinical free text using natural language processing: A systematic review of current approaches—0
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
De-identification of medical records using conditional random fields and long short-term memory networks—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
D\'esidentification de donn\'ees texte produites dans un cadre de relation client (De-identification of customer relationship text data )—0
Detection of Text Reuse in French Medical Corpora—0
Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse—0
Differentially Private Imaging via Latent Space Manipulation—0
Digital Speech Algorithms for Speaker De-Identification—0
DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization—0
Disguise without Disruption: Utility-Preserving Face De-Identification—0
Divide and Conquer: a Two-Step Method for High Quality Face De-identification with Model Explainability—0
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