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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 41–50 of 174 papers

TitleStatusHype
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
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
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
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