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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
An Overview of AI and Blockchain Integration for Privacy-Preserving—0
Medical records condensation: a roadmap towards healthcare data democratisation—0
Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records—0
Medical Image Deidentification, Cleaning and Compression Using Pylogik—0
PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences—0
Disguise without Disruption: Utility-Preserving Face De-Identification—0
Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse—0
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4Code1
k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignmentCode0
Report of the Medical Image De-Identification (MIDI) Task Group -- Best Practices and Recommendations—0
RiDDLE: Reversible and Diversified De-identification with Latent EncryptorCode1
Pedestrian Attribute Editing for Gait Recognition and AnonymizationCode0
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
Hiding Visual Information via Obfuscating Adversarial PerturbationsCode1
De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks—0
DeID-VC: Speaker De-identification via Zero-shot Pseudo Voice ConversionCode1
Open video data sharing in developmental and behavioural science—0
Accurate clinical and biomedical Named entity recognition at scaleCode3
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
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