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
Biomedical Named Entity Recognition at Scale
Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc.
De-identification of Privacy-related Entities in Job Postings
We present JobStack, a new corpus for de-identification of personal data in job vacancies on Stackoverflow.
Towards a Data Privacy-Predictive Performance Trade-off
In this paper, we aim to evaluate the existence of a trade-off between data privacy and predictive performance in classification tasks.
Radiology Text Analysis System (RadText): Architecture and Evaluation
Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis.
Improving speaker de-identification with functional data analysis of f0 trajectories
Due to a constantly increasing amount of speech data that is stored in different types of databases, voice privacy has become a major concern.
Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts
suamin/t2ner
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BioNLP (ACL) 2022
Despite the advances in digital healthcare systems offering curated structured knowledge, much of the critical information still lies in large volumes of unlabeled and unstructured clinical texts.
Accurate clinical and biomedical Named entity recognition at scale
We introduce an agile, production-grade clinical and biomedical Named entity recognition (NER) algorithm based on a modified BiLSTM-CNN-Char DL architecture built on top of Apache Spark.
DeID-VC: Speaker De-identification via Zero-shot Pseudo Voice Conversion
The widespread adoption of speech-based online services raises security and privacy concerns regarding the data that they use and share.
Hiding Visual Information via Obfuscating Adversarial Perturbations
suzhigangssz/avih
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ICCV 2023
Growing leakage and misuse of visual information raise security and privacy concerns, which promotes the development of information protection.
RiDDLE: Reversible and Diversified De-identification with Latent Encryptor
ldz666666/riddle
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CVPR 2023
This work presents RiDDLE, short for Reversible and Diversified De-identification with Latent Encryptor, to protect the identity information of people from being misused.
