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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 76–100 of 174 papers

TitleStatusHype
Audio De-identification - a New Entity Recognition Task—0
Augmenting a De-identification System for Swedish Clinical Text Using Open Resources and Deep Learning—0
Automatic end-to-end De-identification: Is high accuracy the only metric?—0
Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification—0
Personalized and Invertible Face De-Identification by Disentangled Identity Information Manipulation—0
Plausible Deniability for Privacy-Preserving Data Synthesis—0
Privacy-preserving Optics for Enhancing Protection in Face De-identification—0
AnonymousNet: Natural Face De-Identification with Measurable Privacy—0
Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples—0
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification—0
Re-identification of De-identified Documents with Autoregressive Infilling—0
Report of the Medical Image De-Identification (MIDI) Task Group -- Best Practices and Recommendations—0
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing—0
rx-anon -- A Novel Approach on the De-Identification of Heterogeneous Data based on a Modified Mondrian Algorithm—0
SAIC: Integration of Speech Anonymization and Identity Classification—0
Scrubbing Sensitive PHI Data from Medical Records made Easy by SpaCy -- A Scalable Model Implementation Comparisons—0
Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records—0
Smartphone Camera De-identification while Preserving Biometric Utility—0
Spanish Datasets for Sensitive Entity Detection in the Legal Domain—0
Speaker De-identification System using Autoencodersand Adversarial Training—0
Speaker Identification Experiments Under Gender De-Identification—0
StyleGAN as a Utility-Preserving Face De-identification Method—0
The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text—0
The Multilingual Anonymisation Toolkit for Public Administrations (MAPA) Project—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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