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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 11–20 of 174 papers

TitleStatusHype
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion ModelsCode1
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4Code1
Face Identity Disentanglement via Latent Space MappingCode1
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data SharingCode1
RiDDLE: Reversible and Diversified De-identification with Latent EncryptorCode1
Ego4D: Around the World in 3,000 Hours of Egocentric VideoCode1
CIAGAN: Conditional Identity Anonymization Generative Adversarial NetworksCode1
Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical RecordsCode1
Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical TextsCode1
The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text AnonymizationCode1
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