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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 126–150 of 174 papers

TitleStatusHype
PHICON: Improving Generalization of Clinical Text De-identification Models via Data AugmentationCode0
Smartphone Camera De-identification while Preserving Biometric Utility—0
Privacy Guarantees for De-identifying Text TransformationsCode0
Hide-and-Seek Privacy ChallengeCode0
The UU-Net: Reversible Face De-Identification for Visual Surveillance Video FootageCode0
NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical Document De-Identification—0
D\'esidentification de donn\'ees texte produites dans un cadre de relation client (De-identification of customer relationship text data )—0
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical DomainCode0
A Semi-supervised Approach for De-identification of Swedish Clinical Text—0
CodE Alltag 2.0 --- A Pseudonymized German-Language Email Corpus—0
Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation—0
Live Face De-Identification in Video—0
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text—0
Towards De-identification of Legal Texts—0
De-Identification of Emails: Pseudonymizing Privacy-Sensitive Data in a German Email Corpus—0
Augmenting a De-identification System for Swedish Clinical Text Using Open Resources and Deep Learning—0
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning TasksCode0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing—0
Scrubbing Sensitive PHI Data from Medical Records made Easy by SpaCy -- A Scalable Model Implementation Comparisons—0
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical RecordsCode0
Audio De-identification - a New Entity Recognition Task—0
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language ModelsCode0
AnonymousNet: Natural Face De-Identification with Measurable Privacy—0
Publicly Available Clinical BERT EmbeddingsCode0
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