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Adversarial Learning of Privacy-Preserving Text Representations for
  De-Identification of Medical Records

Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records

12 June 2019
Max Friedrich
Arne Köhn
Gregor Wiedemann
Chris Biemann
    OODMedIm
ArXiv (abs)PDFHTML

Papers citing "Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records"

12 / 12 papers shown
Title
Token-Level Privacy in Large Language Models
Reém Harel
Niv Gilboa
Yuval Pinter
84
0
0
05 Mar 2025
Unlocking the Potential of Large Language Models for Clinical Text
  Anonymization: A Comparative Study
Unlocking the Potential of Large Language Models for Clinical Text Anonymization: A Comparative Study
David Pissarra
Isabel Curioso
João Alveira
Duarte Pereira
Bruno Ribeiro
Tomas Souper
Vasco Gomes
A. Carreiro
Vitor Rolla
46
4
0
29 May 2024
Unsupervised Text Deidentification
Unsupervised Text Deidentification
John X. Morris
Justin T. Chiu
Ramin Zabih
Alexander M. Rush
63
7
0
20 Oct 2022
How Much User Context Do We Need? Privacy by Design in Mental Health NLP
  Application
How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application
Ramit Sawhney
A. Neerkaje
Ivan Habernal
Lucie Flek
73
3
0
05 Sep 2022
Differential Privacy in Natural Language Processing: The Story So Far
Differential Privacy in Natural Language Processing: The Story So Far
Oleksandra Klymenko
Stephen Meisenbacher
Florian Matthes
62
16
0
17 Aug 2022
How to keep text private? A systematic review of deep learning methods
  for privacy-preserving natural language processing
How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing
Samuel Sousa
Roman Kern
PILMAILaw
71
46
0
20 May 2022
Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of
  Code-Mixed Clinical Texts
Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts
Saadullah Amin
N. Goldstein
M. Wixted
Alejandro García-Rudolph
Catalina Martínez-Costa
G. Neumann
68
5
0
10 Apr 2022
CAPE: Context-Aware Private Embeddings for Private Language Learning
CAPE: Context-Aware Private Embeddings for Private Language Learning
Richard Plant
Dimitra Gkatzia
V. Giuffrida
80
27
0
27 Aug 2021
De-identification of Privacy-related Entities in Job Postings
De-identification of Privacy-related Entities in Job Postings
Kristian Nørgaard Jensen
Mike Zhang
Barbara Plank
51
16
0
24 May 2021
When Machine Learning Meets Privacy: A Survey and Outlook
When Machine Learning Meets Privacy: A Survey and Outlook
B. Liu
Ming Ding
Sina shaham
W. Rahayu
F. Farokhi
Zihuai Lin
94
294
0
24 Nov 2020
Closing the Gap: Joint De-Identification and Concept Extraction in the
  Clinical Domain
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain
Lukas Lange
Heike Adel
Jannik Strötgen
27
10
0
19 May 2020
Towards De-identification of Legal Texts
Towards De-identification of Legal Texts
Diego Garat
Dina Wonsever
10
1
0
09 Oct 2019
1