De-identification
37 papers with code • 0 benchmarks • 2 datasets
De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.
Benchmarks
These leaderboards are used to track progress in De-identification
Datasets
i2b2 De-identification Dataset
Most implemented papers
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records
maxfriedrich/deid-training-data
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ACL 2019
De-identification is the task of detecting protected health information (PHI) in medical text.
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
MLforHealth/MIMIC_Generalisation
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When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve.
Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
We test the generalizability of three de-identification methods across languages and domains.
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain
boschresearch/joint_anonymization_extraction
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ACL 2020
Exploiting natural language processing in the clinical domain requires de-identification, i. e., anonymization of personal information in texts.
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity.
MASK: A flexible framework to facilitate de-identification of clinical texts
The software is able to perform named entity recognition using some of the state-of-the-art techniques and then mask or redact recognized entities.
The UU-Net: Reversible Face De-Identification for Visual Surveillance Video Footage
hugomcp/uu-net
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We propose a reversible face de-identification method for low resolution video data, where landmark-based techniques cannot be reliably used.
Hide-and-Seek Privacy Challenge
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing.
Privacy Guarantees for De-identifying Text Transformations
Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data.
PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation
De-identification is the task of identifying protected health information (PHI) in the clinical text.
