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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 1–50 of 174 papers

TitleStatusHype
Enhancing Clinical Models with Pseudo Data for De-identificationCode0
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification—0
Re-identification of De-identified Documents with Autoregressive Infilling—0
Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes—0
Palmprint De-Identification Using Diffusion Model for High-Quality and Diverse Synthesis—0
Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish—0
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?—0
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records—0
Data-Constrained Synthesis of Training Data for De-Identification—0
Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts—0
Zero-shot generation of synthetic neurosurgical data with large language modelsCode0
The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray GenerationCode0
Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images—0
SVIA: A Street View Image Anonymization Framework for Self-Driving ApplicationsCode0
Enhancing the De-identification of Personally Identifiable Information in Educational DataCode1
LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and De-identification of PHI Across Multiple Languages—0
Medical Manifestation-Aware De-IdentificationCode0
Face De-identification: State-of-the-art Methods and Comparative Studies—0
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal MessagesCode0
Large Language Models for Patient Comments Multi-Label Classification—0
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. PerformanceCode0
DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization—0
De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient PrivacyCode1
DeIDClinic: A Multi-Layered Framework for De-identification of Clinical Free-text DataCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
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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