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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 26–50 of 174 papers

TitleStatusHype
Generation and De-Identification of Indian Clinical Discharge Summaries using LLMsCode0
Automated Privacy-Preserving Techniques via Meta-LearningCode0
GiusBERTo: A Legal Language Model for Personal Data De-identification in Italian Court of Auditors Decisions—0
AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework—0
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data SharingCode1
CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image FormatsCode2
EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model—0
Computational Job Market Analysis with Natural Language ProcessingCode0
Privacy-preserving Optics for Enhancing Protection in Face De-identification—0
RID-TWIN: An end-to-end pipeline for automatic face de-identification in videosCode0
A Privacy-Preserving Unsupervised Speaker Disentanglement Method for Depression Detection from SpeechCode0
De-identification is not always enough—0
ToonerGAN: Reinforcing GANs for Obfuscating Automated Facial Indexing—0
SAIC: Integration of Speech Anonymization and Identity Classification—0
Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets—0
VerA: Versatile Anonymization Applicable to Clinical Facial Photographs—0
De-identification of clinical free text using natural language processing: A systematic review of current approaches—0
Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification—0
Privacy Protection in MRI Scans Using 3D Masked Autoencoders—0
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion ModelsCode1
Generative Adversarial Networks for Dental Patient Identity Protection in Orthodontic Educational Imaging—0
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis—0
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistencyCode0
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market DomainCode1
In the Name of Fairness: Assessing the Bias in Clinical Record De-identificationCode0
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